JMIR FORMATIVE RESEARCH

Yang et al

Original Paper

Visual Function and Driving Performance Under Different Lighting
Conditions in Older Drivers: Preliminary Results From an
Observational Study
Jingzhen Yang1,2, MPH, PhD; Enas Alshaikh1, PhD; Deyue Yu3, PhD; Thomas Kerwin4, PhD; Christopher Rundus1,
PhD; Fangda Zhang1, PhD; Cameron G Wrabel4, BS; Landon Perry3, OD; Zhong-Lin Lu5,6,7, PhD
1Center for Injury Research and Policy at the Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United States

2

Department of Pediatrics, The Ohio State University, Columbus, OH, United States

3

College of Optometry, The Ohio State University, Columbus, OH, United States

4

Driving Simulation Laboratory, The Ohio State University, Columbus, OH, United States

5

Division of Arts and Sciences, NYU Shanghai, Shanghai, China

6Center for Neural Science and Department of Psychology, New York University, New York, NY, United States

7

NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai, China

Corresponding Author:
Jingzhen Yang, MPH, PhD
Center for Injury Research and Policy at the Abigail Wexner Research InstituteNationwide Children’s Hospital
700 Children's Drive
Columbus, OH, 43205
United States
Phone: 1 6143555852
Email: Ginger.Yang@nationwidechildrens.org

Abstract
Background: Age-related vision changes significantly contribute to fatal crashes at night among older drivers. However, theeffects of lighting conditions on age-related vision changes and associated driving performance remain unclear.
Objective: This pilot study examined the associations between visual function and driving performance assessed by a high-fidelity
driving simulator among drivers 60 and older across 3 lighting conditions: daytime (photopic), nighttime (mesopic), and nighttime
with glare.
Methods: Active drivers aged 60 years or older participated in visual function assessments and simulated driving on a high-fidelity
driving simulator. Visual acuity (VA), contrast sensitivity function (CSF), and visual field map (VFM) were measured using
quantitative VA, quantitative CSF, and quantitative VFM procedures under photopic and mesopic conditions. VA and CSF werealso obtained in the presence of glare in the mesopic condition. Two summary metrics, the area under the log CSF (AULCSF)
and volume under the surface of VFM (VUSVFM), quantified CSF and VFM. Driving performance measures (average speed,
SD of speed [SDspeed], SD of lane position (SDLP), and reaction time) were assessed under daytime, nighttime, and nighttime
with glare conditions. Pearson correlations determined the associations between visual function and driving performance acrossthe 3 lighting conditions.
Results: Of the 20 drivers included, the average age was 70.3 years; 55% were male. Poor photopic VA was significantly
correlated with greater SDspeed (r=0.26; P<.001) and greater SDLP (r=0.31; P<.001). Poor photopic AULCSF was correlatedwith greater SDLP (r=–0.22; P=.01). Poor mesopic VUSFVM was significantly correlated with slower average speed (r=–0.24;
P=.007), larger SDspeed (r=–0.19; P=.04), greater SDLP (r=–0.22; P=.007), and longer reaction times (r=–0.22; P=.04) whiledriving at night. For functional vision in the mesopic condition with glare, poor VA was significantly correlated with longer
reaction times (r=0.21; P=.046) while driving at night with glare; poor AULCSF was significantly correlated with slower speed(r=–0.32; P<.001), greater SDLP (r=–0.26; P=.001) and longer reaction times (r=–0.2; P=.04) while driving at night with glare.
No other significant correlations were observed between visual function and driving performance under the same lighting
conditions.

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Conclusions: Visual functions differentially affect driving performance in different lighting conditions among older drivers,
with more substantial impacts on driving during nighttime, especially in glare. Additional research with larger sample sizes isneeded to confirm these results.
(JMIR Form Res 2024;8:e58465) doi: 10.2196/58465
KEYWORDS
nighttime driving; functional vision; driving simulation; older drivers; visual functions; photopic; mesopic; glare; driving simulator

Introduction
Drivers aged 65 years and older face a heightened risk of fatalnighttime crashes, surpassed only by drivers aged under 25
years, based on fatal nighttime crashes per distance driven [1-3].
Anatomical and functional changes in vision due to aging
contribute significantly to this elevated risk [4-9], resulting in
a 2-4 times greater likelihood of fatal nighttime crashes thandaytime incidents [2,5]. Concerns among older drivers regardingnighttime driving include heightened glare from oncoming
headlights [10,11], slower recovery times after experiencing
glare [12], poor recognition of road signs at night [13], anddifficulty differentiating contrast in glare conditions [7,14].
Notably, approximately 1 in 3 older drivers restricts or ceasesnighttime driving due to these concerns [15,16]. Moreover,various eye diseases prevalent among older adults, such as
cataracts, glaucoma, and age-related macular degeneration,
further compromise their ability to drive safely at night [17-19].

including slower average speed, a greater standard deviation of
speed (SDSpeed), a greater standard deviation of lane position(SDLP), and longer reaction times to visual stimuli.

Methods
Study Design and Participants
We conducted a pilot study with 20 active drivers aged 60 years
or older recruited from the Columbus, Ohio, area. All
participants held valid driver’s licenses and were active drivers,
defined as those who drove at least once per week. We excluded
drivers who drove less than once per week, used visual aid
devices beyond habitual correction (eg, bioptic lens), or had
conditions preventing them from driving, such as cognitive
impairment or eye diseases. Non-English speakers andindividuals with a history of severe motion sickness were also
excluded. The study data were collected between November
2021 and August 2022.

Despite these challenges, current clinical examinations of vision
primarily concentrate on visual acuity (VA) under daytime
photopic lighting conditions, disregarding the mesopic (lowlighting) and glare conditions encountered during nighttime
driving [20,21]. Additionally, the contrast sensitivity function(CSF) [22,23], which quantifies how contrast sensitivity(1/threshold) changes with spatial frequency, is more closely
related to performance in daily visual tasks in general [24,25]
and driving in particular [14,26-29]. The visual field map (VFM)
is another measure that could offer critical insights intofunctional vision and performance during nighttime driving.
The gap in visual assessment poses a significant issue, especiallygiven the substantial increase in the older adult driving
population. In 2020, the 48 million licensed drivers aged over
65 years in the United States represented a 68% increase since2000 [11]. Projections suggest that by 2050, 1 in every 4
licensed drivers will be aged 65 or older [19,30]. Therefore,
understanding how age-related vision changes affect driving
performance among drivers aged 65 and older is imperative,
especially considering the various lighting conditions theyencounter [28,31,32]. This knowledge will pave the way fordeveloping and implementing therapeutic interventions andtechnological assistance to support these drivers in maintaining
their driving privileges and ensuring road safety.

Study Procedures

To address this research gap, our pilot study examined theassociations between visual function and driving performance,
assessed by a high-fidelity driving simulator, among drivers 60
and older across 3 lighting conditions: daytime (photopic),nighttime (mesopic), and nighttime with glare. We hypothesizedthat drivers with worse visual function, measured by VA, CSF,
and VFM, would exhibit worse simulated driving performance,

Visual Function Test

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We distributed information about our study through channels
that had proven successful in our previous research: (1) a
website listing and Facebook advertising via StudySearch, a
directory managed by the Ohio State University (OSU) Centerfor Clinical and Translational Science [33]; (2) ResearchMatch,a registry of volunteers managed by the Vanderbilt Institutes
for Clinical and Translational Research, spanning a network of
183 institutions [34]; (3) Twitter and Instagram posts facilitatedby Nationwide Children’s Hospital; and (4) study flyers
distributed at local community centers. Social media postingsor email links directed interested individuals to complete an
initial eligibility survey on our study website or to meet ourstudy team over the phone for eligibility screening.
Following expressions of interest, our study coordinator
contacted the individuals, rescreened them for eligibility, andobtained signed consent. Subsequently, the study coordinator
scheduled an in-person appointment for visual function
assessments at the OSU College of Optometry’s Laboratory of
Vision Enhancement. Additionally, participants underwent adriving performance assessment on a high-fidelity driving
simulator at the OSU Driving Simulation Laboratory. These 2
assessments took place within 1 month of each other.

Overview
The visual function test was conducted binocularly withparticipants wearing the habitual corrective lenses they used fordaily driving. The test encompassed 3 fundamental visualfunctions: VA, CSF, and VFM, measured using the quantitative
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VA, quantitative CSF (qCSF), and quantitative VFM (qVFM)
methods, respectively [35-39]. These methods have used an
active learning framework to provide efficient assessments while
maintaining high precision and accuracy, and they have beenvalidated in approximately 50 peer-reviewed publications,demonstrating high correlations with conventional visualfunction tests as well as precision and repeatability across thefull spectrum of spatial frequencies [37,40-44]. For example,
in the quantitative VA task, participants were instructed to
identify 3 high-contrast letters in each trial, with the letter size
varying between trials. The same applied to the qCSF task, withthe exception that the contrast of letters also varied between
letters and trials. During the qVFM task, participants weredirected to report a light spot that might have briefly presented
inside a circular location cue while keeping their gaze fixed in
the center of the display for the duration of the test. The target
location and luminance were updated in an adaptive manner.
The size of the visual field being evaluated was 48°×48°. Twosummary metrics, the area under the log contrast sensitivity
function (AULCSF) and the volume under the surface of VFM(VUSVFM), scored the CSF and VFM tests. The tests wereconducted in 2 or 3 lighting conditions, described in thefollowing sections.

mesopic measurements. After more than a 5-minute dark
adaptation period, participants underwent measurements formesopic VA, CSF, and VFM using the same methods as
photopic assessments.

Photopic Condition

The driving performance test on a cutting-edge real-time
technology driving simulation platform and lasted approximately
1 hour (Figure 1). This system features a vehicle cab mounted
on a 6-degree freedom of motion base, a 260° front-projection
cylindrical edge-blended screen, side mirror LCDs, and a rearmirror screen. The custom-developed driving scenarios consistedof driving on a straight, suburban roadway with a posted speedof 72 km/h.

2Background luminance was set at 9 cd/m or above for all thephotopic tests, encompassing VA, CSF, and VFM
measurements. A brief practice block was given for each task.
A minimum of 5 minutes of dark adaptation was given before
starting the first practice.
Mesopic Condition

Mesopic With Glare Tests
Mesopic VA and CSF were measured in the presence of glare.
An external light source, a Fiilex V70 lamp with an attacheddome diffuser, generated glare. The light was placed at theparticipant’s eye level, with 19 cm in front and 12 cm to the leftof the midpoint of the participant’s 2 eyes. The glare source hada color temperature of 3000 K and provided an illuminationlevel of 305 lux, similar to home or office desk lighting (30-1000lux) [45].
VA was always measured first for each lighting condition,
followed by CSF, with VFM as the last measurement taken
under photopic and mesopic conditions. Thus, 8 visual function
tests were performed, generating VA, AULCSF, and VUSVFM
values across 3 lighting conditions. Low VA values and high
AULCSF and VUSVFM values indicated better visual function.

Driving Performance Test
Driving Simulator

Background luminance was reduced to between 0.1 and 1 cd/m2
using a neutral density filter or display control, yielding reliable

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Figure 1. A high-fidelity driving simulator.

Three Experimental Lighting Conditions
We used 3 experimental lighting conditions to provide
participants with scenarios and experiences in a safe, controlledenvironment [27,46,47]. Participants drove with 3 conditions
in a random sequence: a daytime condition which met therecommended photopic lighting requirements with lighting inthe scene coming from headlights from the participant vehicle,
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to meet the mesopic light level, and nighttime with glare
condition which used a point glare source (ie, a lamp fixed tothe hood of the car) positioned on the left side of the carsimulating headlight glare from oncoming vehicles, allowing
the participants to experience the simulated bright headlights
shining at the driver from cars in the opposing travel direction.
The lamp used was a 650-lumen light-emitting diode bulb

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positioned approximately 2.1 meters away from the driver’s
eye point, 4 degrees to the driver's left.

of safe driving performance in numerous driving simulation
studies [53].

Simulator Procedure

Other Variables

Upon arrival at the OSU Driving Simulation Laboratory,
participants completed a 10-minute paper survey about theirdemographics, motion sickness, history of eye disease, driving
habits, and functional impairments before the driving
assessment. We advised participants to inform staff if theyexperienced motion sickness, dizziness, or discomfort whiledriving on the simulator and to stop the assessment if necessary.

Gathered demographic information included age, sex, race,
marital status, employment, income level, self-assessed overall
health, and eye health. Self-reported driving history included
average hours driven per day and week and levels of difficulties
while driving in various situations in dim light or at night.

Participants drove a total of 35 minutes in the driving simulator.
Following a 5-minute acclimation period, participants completed
3 drives, 1 for each lighting condition, each lasting
approximately 10 minutes. The drive sequence was shuffled
per participant for counterbalancing. We asked participants todrive as they usually would on a 2-way, undivided roadway
with 1 lane in each direction of traffic at a speed limit of 72
km/h. Simulated trees, signs, and buildings were presented on
both sides of the road. Participants performed general driving
maneuvers, including regulating vehicle speed and direction,lane positioning, and maintaining safe following distance, underlight traffic in the opposing lane for all the driving scenarios.
During the driving scenarios, participants also encounteredhalf-meter-sized boxes appearing randomly above the roadway
in different locations: 1 with a vertical stripe and the other witha horizontal stripe. Participants were instructed to press a buttonon the steering wheel when they saw a box with only ahorizontal stripe.
To regulate the driving speed to around 72 km/h, participants
were instructed to stay within 30 meters of a lead car. If theywent slower than 64 km/h, the lead car would drive ahead; ifthey moved faster than 80 km/h, the lead vehicle would not
accelerate further. This adaptation helped maintain theparticipants’ speed between 64 and 80 km/h. During all thedriving scenarios, participants also completed a cognitive load
task [48], which consisted of conversations between an
experimenter and the participant about general interest topics,
like travel or food. The experimenter spoke to the participantsfrom a control room through an intercom.

Driving Performance Outcome Measures
We measured driving performance using 4 primary variables:
average speed (km/h), SDSpeed [49], SDLP [50,51], andreaction time [52]. Reaction time is defined as the time between
a target box appearing above the roadway and the participant
pressing a button on the steering wheel. Only the reaction time
for correct responses (horizontal stripe) was included in theanalysis. The driving performance-related variables were
measured in multiple periods per drive, surrounding each box
appearance, and averaged over the drive. SDSpeed is a measureof the longitudinal control of the vehicle, while SDLP is ameasure of the lateral control of the vehicle. SDLP, as a measureof vehicle control in particular, has been used as an indicator

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Statistical Analysis
We conducted a descriptive analysis of participants’demographics and driving variables. We excluded 2 participantsfrom the analyses due to unusable driving data resulting fromprocedure adjustment or technical difficulties. We used
ANOVA, with Bonferroni correction for multiple comparisons,to compare vision scores across 3 lighting conditions.
Additionally, we used ANOVA with repeated measures to assessthe driving performance variable across 3 lighting conditions.
We calculated Pearson correlation coefficients to evaluate therelationships between visual function and driving performance
under each lighting condition, and we used the false discovery
rate for multiple testing. We conducted all data analyses in SAS
and completed the analyses by January 2024, with a significancelevel of α=.05.

Ethical Considerations
The study received ethical approval from the Institutional
Review Board at Nationwide Children’s Hospital(STUDY00000461). Written informed consent was obtained
from all participants. The study was performed in accordancewith the ethical standards as laid down in the Declaration of
Helsinki and its later amendments or comparable ethicalstandards and the applicable local regulatory requirements andlaws.

Results
Characteristics of Participants
Across all 20 participants, the average age was 70.3 (ranging
from 63 to 83), with 45% (n=9) female. A total of 6 participants
had had cataract surgery, and 1 participant received a cataract
diagnosis but had no surgical intervention. On average,
participants had held their driver’s license for about 55 years,
and self-reported eyesight averaged 7.6 on a scale of 1-10. In
total, 50% (n=10) of the participants reported driving difficulties,
ranging from a little difficulty to extreme difficulty, due toconcerns about their eyesight in dim light or at night.
Additionally, 45% reported driving less in dim light or at night
because of their eyesight (Figure 2A). Participants indicatedthat they avoided driving in dim light or at night due to moderateor extreme difficulties, including driving in difficult conditions(n=8, 40%), driving in unfamiliar places (n=6, 30%), noticing
objects off to the side (n=5, 25%), or reading street signs (n=4,20%) (Figure 2B).

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Figure 2. (A) Level of nighttime driving vision difficulties. (B) Frequency of changes in driving habits.

Visual Functions in 3 Lighting Conditions
The average VA was –0.07 (SD 0.08) in the photopic (daytime)
condition, significantly lower (indicating better function) than
the average VA in the mesopic (nighttime) condition (mean0.23, SD 0.11; P<.001) and in the mesopic with glare condition(mean 0.44, SD 0.17; P<.001) (Table 1). The average AULCSF
was 1.51 (SD 0.27) in the photopic condition, significantly
greater (indicating better function) than the average AULCSF

in the mesopic condition (mean 0.081, SD 0.20; P<.001) andin the mesopic with glare condition (mean 0.32, SD 0.21;
P<.001), which was the worst among the 3 lighting conditions.
Finally, the average VUSVFM in the photopic condition was
907.08 (SD 47.05); while lower (indicating worse function)
than in the mesopic condition (mean 917.89, SD 78.60; P=.60),
this difference was statistically insignificant. The lighting
conditions accounted for 74%, 83%, and 9% of the variation of
VA, AULCSF, and VUSVFM, respectively.

Table 1. Visual function scores across 3 different lighting conditions.

aVisual function

Overall score (n=60),
mean (SD)Photopic score (n=20),
mean (SD)

Mesopic score (n=20),
mean (SD)Glare score (n=20),
mean (SD)R2

P valuea

VAb

0.2 (0.25)–0.07 (0.08)

0.23 (0.11)

0.44 (0.17)

0.74

<.001

AULCSFc

0.88 (0.54)

1.51 (0.27)

0.81 (0.2)

0.32 (0.21)

0.83

<.001

VUSVFMd,e912.43 (64.16)

907.08 (47.05)

917.89 (78.60)

N/Af

0.09

.60

P value testing the differences across vision conditions.

b

VA: visual acuity.

c

AULCSF: area under the log contrast sensitivity function.

d

VUSVFM: volume under the surface of the visual field map.

e

n=40.

f

N/A: not applicable (VUSVFM was not measured in the presence of glare).

Driving Performance Under 3 Lighting Conditions
Of the 18 participants included, the average driving speed was
71.55 (SD 8.89) km/h in the daytime condition, significantly
slower than that in the nighttime with glare condition (74.60,
SD 13.97 km/h) but not significantly different from that in thenighttime without glare condition (73.71, SD 16.33 km/h; P=.28)(Table 2). The average SDLP in the nighttime with glare

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condition was 0.11 (SD 0.07) meters, significantly greater thanthat in the daytime (mean 0.07, SD 0.04 meters; P<.001) andthe nighttime without glare condition (mean 0.08, SD 0.04
meters; P<.001). Furthermore, the average reaction time was
1.23 (SD 0.29) and 1.21 (SD 0.38) seconds in the nighttime
conditions with and without glare, both significantly longer than
the average reaction time in the daytime condition (mean 1.07,
SD 0.31 seconds; P<.001).

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Table 2. Driving performance scores across 3 different lighting conditions.
Driving performanceaOverall score (n=591),
mean (SD)

Daytime (n=210), mean Nighttime (n=181),(SD)
mean (SD)Nighttime with glare(n=200), mean (SD)P value

Average speed (km/h) 73.24 (13.30)

71.55a (8.89)

73.71 (16.33)

74.60 (13.97).002

SDSpeeda

0.47 (0.61)

0.42 (0.35)

0.51 (0.92)

0.47 (0.47).25

SDLPb (meters)

0.08 (0.06)

0.07 (0.04)

0.08 (0.04)

0.11c,d (0.07)<.001

Reaction timee (seconds)

1.16 (0.34)f

1.07c,g (0.31)h

1.21 (0.38)i

1.23 (0.29)j<.001

SD of speed.

b

SDLP: SD of lane position.

c

Significant differences between daytime and nighttime with glare.

dSignificant differences between nighttime and nighttime with glare.

eTime between a target box with a stripe appearing above the roadway and the participant pressing a button on the steering wheel for a correct response(horizontal stripe).
fn=448.

g

Significant differences between daytime and nighttime.

hn=182.

in=138.

jn=128.

P<.001) and greater SDLP (r=0.31; P<.001) (Figure 3A). Poor
AULCSF was associated with greater SDLP (r=–0.22; P=.007)(Figure 3B).

Correlations Between Visual Function and Driving
Performance in 3 Lighting Conditions
For functional vision in the photopic condition (Table 3), poor
VA was significantly correlated with greater SDspeed (r=0.26;

Table 3. Pearson correlation coefficients between visual function and driving performance scores across 3 lighting conditions.
Visualfunction

SDLPb

Nighttime (mesopic) correlation coefficients

Nighttime with glare (glare) correlation coefficients

SDLP

time

Average SDSpeed
Speed

SDLP

time

Average SDSpeed
speed

Average
speed

SDSpeeda

0

0.26e

0.31e

0.08

–0.03

0.08

0.13

0.17

0.12

–0.04

0.16

0.21f

AULCSFg 0.08

0.05

–0.22e

–0.12

0.09

–0.08

–0.12

–0.17

–0.32e–0.09–0.26e–0.20f

VUSVFMh –0.03

–0.05

–0.13

–0.16

–0.24d–0.19e–0.22e –0.22e

N/Ai

N/A

N/A

N/A

VAd

a

Daytime (photopic) correlation coefficients
ReactioncReactioncReaction
timec

SDSpeed: SD of speed.

b

SDLP: SD of lane position.

cTime between a target box with a stripe appearing above the roadway and the participant pressing a button on the steering wheel for a correct response(horizontal stripe).
d

VA: visual acuity.

e

P value <.01.

f

P value <.05.

g

AULCSF: area under the log contrast sensitivity function.

h

VUSVFM: volume under the surface of the visual field map.

i

N/A: VUSVFM was not measured in the presence of glare.

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Figure 3. Correlations between visual function and driving performance in 3 lighting conditions. (A) Correlations of VA with SDLP. (B) Correlationsof AULCSF with SDLP. (C) Correlations of VUSVFM with SDLP. (D) Correlations of VA with reaction time. (E) Correlations of AULCSF withreaction time. (F) Correlations of VUSVFM with reaction time. SDLP: SD of lane position; VA: visual acuity; AULCSF: area under the log contrast
sensitivity function; VUSVFM: volume under the surface of the visual field map.

For functional vision in the mesopic condition (Table 3), poor
VUSFVM was significantly correlated with slower average
speed (r=–0.24; P=.007) and larger SDSpeed (r=–0.19; P=.04),
greater SDLP (r=–0.22; P=.01) (Figure 3C), and longer reaction
times (r=–0.22; P=.04) (Figure 3F) while driving at night.
For functional vision in the mesopic condition with glare (Table3); Poor VA was significantly correlated with longer reaction
times (r=0.21; P=.046) while driving at night with glare (Figure
3D). Poor AULCSF was significantly correlated with slower
speed (r=–0.32; P<.001), greater SDLP (r=–0.26; P<.001)(Figure 3B), and longer reaction times (r=–0.2; P=.04) (Figure3E) while driving at night with glare. No other significant
correlations were observed between visual function and driving
performance under the same light conditions.
Furthermore, poor photopic VA was significantly correlatedwith greater SDLP while driving at night with (r=0.25; P<.001)
and without (r=0.28; P<.001) glare, respectively [see Table S1
in Multimedia Appendix 1]. Poor photopic AULCSF was
significantly correlated with greater SDLP across the 3 lighting
conditions, longer reaction times (r=–0.24; P<.001) whiledriving at night, and larger SDSpeed (r=–0.41; P<.001) whiledriving at night with glare. Poor photopic VUSFVM wassignificantly correlated with a larger SDLP (r=–0.25; P<.001)
while driving at night with glare. For functional vision in themesopic condition, poor VA, AULCSF, and VUSFVM weresignificantly correlated with greater SDLP and/or longer reaction
times while driving at night with or without glare. For functional
vision in the mesopic condition with glare, poor VA and
AULCSF were correlated with longer reaction times in all 3
lighting conditions.

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Discussion
Key Findings
This pilot study examined the relationships between visualfunction and driving performance across 3 lighting conditions
among drivers aged 60 years and older. The main findings
highlight that worse visual function and driving performance
were observed in the nighttime condition relative to the daytime
condition, particularly with glare. Poor visual functions werelargely correlated with poor driving performance. Specifically,poor photopic VA and CSFs were significantly associated withgreater SDLP (poor lane keeping ability) while driving in all 3
lighting conditions. Poor mesopic VA, CSFs, and VFM weresignificantly correlated with greater SDLP and longer reaction
times while driving at night with or without glare. Poor VA and
CSFs under mesopic conditions with glare were significantly
correlated with longer reaction times in all 3 lighting conditions.
Our results contribute to the current literature on therelationships between visual functions and nighttime driving
and have potential implications for the nighttime driving safetyof over 45 million licensed drivers who are aged 65 years andolder [11].
Crash statistics indicate that the nighttime driving environmentis dangerous for all road users, particularly older drivers [19,54].
Our findings also reveal that participants’ SDLP was
significantly greater at night with glare than in the other 2
lighting conditions. Participants also took longer to respond to
stimuli (eg, reacting to the boxes appearing above the roadway)
during both nighttime conditions compared to the daytime
condition. The observed decline in driving performance amongolder drivers during the nighttime could be attributed to reduced
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including reductions in rod sensitivity, slower dark adaptation,
decreased VA, and increased sensitivity to glare [5,6,8,9]. All
these factors contribute to an increased risk of nighttime crashes[5,53]. Given that access to transportation by car is crucial for
the well-being and independence of many older individuals,
future studies using comprehensive vision tests are imperative.
Such studies can help identify robust and unambiguous
indicator(s) of older individuals’ fitness to drive at night,
ensuring their driving privileges and road safety [7,19,56,57].

in older drivers in the context of distinct lighting challenges[13,20]. These strategies include considering using polarizing
eyeglasses or other glare mitigation techniques [58]. Futurestudies should also advocate for including novel vision tests
that mimic the effects of nighttime conditions and a glare source[5,27,48]. Such assessments may become imperative due todemographic changes (ie, longer life expectancy) compounded
by increased dependency on private car journeys among older
people [59,60].

Consistent with previous findings [5-7,28], this study reveals
that poor VA and poor CSF were associated with increased
SDLP across all 3 lighting conditions—an indicator of poorvehicle control. Moreover, this study found that individuals
with poor VA, CSF, and VFM may face greater challenges inmaintaining a stable lateral position on the road and take arelatively long time to respond to an event critical to driving
safety, particularly while driving at night. These results suggestthat compromised visual functions may lead to driving
challenges at night, particularly with glare [6]. Therefore,
assessing VA alone during daylight, as current standard vision
tests do, without considering the effect of nighttime vision orglare, may inaccurately characterize functional vision [6].

This study has several limitations. First, the small sample size
and the absence of a control group, along with the lack of dataon participants’ other health-related conditions (eg,comorbidities), restricted our ability to conduct more advanced
statistical analyses. Adjusting for potential known confounding
factors, such as participant age, motor function, and cognitive
capacity, was limited. Second, our results may be susceptible
to selection bias, possibly reflecting a more active or healthier
subset of older drivers. Therefore, caution is needed whengeneralizing our findings to all drivers aged 60 years and older.
Third, the driving measured in this study relied on simulated
driving performance in a high-fidelity driving simulator, which
may not fully reflect participants’ real-world driving behaviors.
Consequently, a cautious interpretation of our results isnecessary. Fourth, the participants completed all visual function
tests while sitting in a stable chair rather than in a moving
vehicle. Thus, the observed relationships between visual function
and driving performance in this study may be under- or
overestimated. Despite these limitations, our study contributes
empirical data on visual function and driving across 3 lighting
conditions. We used a set of comprehensive and sensitive visualfunction measures coupled with simulated driving performanceassessed on a high-fidelity driving simulator.

Puell et al [14] observed that reduced contrast sensitivity,
especially in nighttime conditions with glare, might contributeto challenges during night driving. Wood and Owens [6] foundthat visibility significantly diminishes during night driving, with
contrast sensitivity tests better predicting real-world object
recognition performance than VA alone. This study also foundthat visual function in the mesopic and mesopic with glare
conditions was significantly associated with driving performanceat night, particularly with glare conditions. Poor vision in
low-light conditions is correlated with poor recognition of traffic
signs, as low lighting reduces the reading distance of road signs,
leaving less time to react. These effects are especiallypronounced in older drivers who drive at night [7]. Our results,
along with those from other studies [6,7,14], suggest thatassessing VA, along with CSF, VFM, and other visual functions,is crucial for characterizing age-related vision changes whenevaluating the relationships between vision and driving. These
metrics could also serve as targets for interventions to mitigate
driving challenges, especially at night, among older adults[27,29].
This pilot study is the first to assess the relationships between
visual function and driving performance under different lighting
conditions among drivers aged 60 years and older.
Understanding the impacts of age-related visual deficits onnighttime driving is critically needed because age-related change
in vision is the most significant risk factor for crash-related
injuries and deaths at night in older individuals [4-9]. Future
research and practical applications could explore developing
targeted strategies for addressing specific visual impairments

Limitations

Conclusions
Drivers aged 60 years and older face a heightened risk of fatalnighttime crashes, primarily attributed to age-related visionchanges. This pilot study presents preliminary data indicatingthat VA, contrast sensitivity, and VFM may distinctly impact
driving performance in various lighting conditions among older
drivers, particularly at night and in the presence of glare. Further
research with a larger and more diverse sample is essential tovalidating these results. Our results underscore the importanceof comprehensive visual function assessments, especially undernighttime and glare conditions, for evaluating age-related visionchanges. Additionally, the study findings advocate fordeveloping therapeutic interventions and technological aids toenhance safe nighttime driving among older drivers. As theolder adult population is expected to nearly double by 2050,
increased research efforts in this domain are warranted to guideclinical practices and policies, ensuring the maintenance ofdriving privileges and road safety for individuals aged 60 years
and older.

Acknowledgments
This study was supported by the OSU President’s Research Excellence Accelerator Grant program as well as partially by NEI
R01 EY025658. The content is solely the authors' responsibility and does not necessarily represent the official views of the funder.
We acknowledge the invaluable contributions of the individual drivers who participated in this project.
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Data Availability
The data sets generated or analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions
JY, DY, TK, and ZLL initiated and conceived the study. JY, DY, and TK obtained the funding and developed the recruitment
procedures and data collection methods. DY, LP, TK, and CGW collected the study data. EA conducted data analysis incollaboration with JY and ZLL. JY, EA, CR, and FZ drafted the initial manuscript and critically revised the manuscript. All
authors critically reviewed and revised the manuscript. All authors read and approved the final version of this protocol.

Conflicts of Interest
DY and ZLL own intellectual property rights on the qVFM technology. ZLL holds intellectual property interests in visual function
measurement, rehabilitation technologies, and equity interests in Adaptive Sensory Technology, Inc and Jiangsu Juehua Medical
Technology, Ltd.

Multimedia Appendix 1
Table S1. Pearson correlation coefficients between visual function and driving performance scores across three lighting conditions.
[DOCX File , 17 KB-Multimedia Appendix 1]

References
1.
2.
3.
4.
5.
6.
7.
8.
9.

10.
11.
12.
13.
14.

15.
16.
17.

18.

Web-based injury statistics query and reporting system (WISQARS). US Centers for Disease Control and Prevention. 2018.
URL: https://wisqars.cdc.gov/ [accessed 2024-05-28]
Massie DL, Campbell KL, Williams AF. Traffic accident involvement rates by driver age and gender. Accid Anal Prev.
1995;27(1):73-87. [doi: 10.1016/0001-4575(94)00050-v] [Medline: 7718080]
Cicchino JB, McCartt AT. Trends in older driver crash involvement rates and survivability in the United States: an update.
Accid Anal Prev. 2014;72:44-54. [doi: 10.1016/j.aap.2014.06.011] [Medline: 25003969]
Boot WR, Stothart C, Charness N. Improving the safety of aging road users: a mini-review. Gerontology. 2014;60(1):90-96.
[FREE Full text] [doi: 10.1159/000354212] [Medline: 23989044]
Kimlin J, Black AA, Wood JM. Nighttime driving in older adults: effects of glare and association with mesopic visualfunction. Invest Ophthalmol Vis Sci. 2017;58(5):2796-2803. [doi: 10.1167/iovs.16-21219] [Medline: 28564700]
Wood JM, Owens DA. Standard measures of visual acuity do not predict drivers' recognition performance under day ornight conditions. Optom Vis Sci. 2005;82(8):698-705. [doi: 10.1097/01.opx.0000175562.27101.51] [Medline: 16127335]
Gruber N, Mosimann UP, Müri RM, Nef T. Vision and night driving abilities of elderly drivers. Traffic Inj Prev.
2013;14(5):477-485. [FREE Full text] [doi: 10.1080/15389588.2012.727510] [Medline: 23683029]
Andersen GJ. Aging and vision: changes in function and performance from optics to perception. Wiley Interdiscip Rev
Cogn Sci. 2012;3(3):403-410. [FREE Full text] [doi: 10.1002/wcs.1167] [Medline: 22919436]
Kaleem MA, Munoz BE, Munro CA, Gower EW, West SK. Visual characteristics of elderly night drivers in the salisbury
eye evaluation driving study. Invest Ophthalmol Vis Sci. 2012;53(9):5161-5167. [FREE Full text] [doi: 10.1167/iovs.12-9866][Medline: 22743327]
Kimlin JA, Black AA, Djaja N, Wood JM. Development and validation of a vision and night driving questionnaire.
Ophthalmic Physiol Opt. 2016;36(4):465-476. [doi: 10.1111/opo.12307] [Medline: 27350185]
Beck LF, Luo F, West BA. Examining patterns of driving avoidance behaviors among older people using latent classanalysis. J Appl Gerontol. 2022;41(7):1752-1762. [FREE Full text] [doi: 10.1177/07334648221086953] [Medline: 35441554]
Collins M. The onset of prolonged glare recovery with age. Ophthalmic Physiol Opt. 1989;9(4):368-371. [Medline: 2631002]
Owens D, Wood JM, Owens JM. Effects of age and illumination on night driving: a road test. Hum Factors.
2007;49(6):1115-1131. [doi: 10.1518/001872007X249974] [Medline: 18074710]
Puell MC, Palomo C, Sánchez-Ramos C, Villena C. Mesopic contrast sensitivity in the presence or absence of glare in alarge driver population. Graefes Arch Clin Exp Ophthalmol. 2004;242(9):755-761. [doi: 10.1007/s00417-004-0951-6][Medline: 15290150]
Lyman JM, McGwin G, Sims RV. Factors related to driving difficulty and habits in older drivers. Accid Anal Prev.
2001;33(3):413-421. [doi: 10.1016/s0001-4575(00)00055-5] [Medline: 11235803]
Naumann RB, Dellinger AM, Kresnow M. Driving self-restriction in high-risk conditions: how do older drivers compare
to others? J Safety Res. 2011;42(1):67-71. [doi: 10.1016/j.jsr.2010.12.001] [Medline: 21392632]
Janz NK, Musch DC, Gillespie BW, Wren PA, Niziol LM, Collaborative Initial Glaucoma Treatment Study (CIGTS)
Investigators. Evaluating clinical change and visual function concerns in drivers and nondrivers with glaucoma. Invest
Ophthalmol Vis Sci. 2009;50(4):1718-1725. [FREE Full text] [doi: 10.1167/iovs.08-2575] [Medline: 19060263]
Owsley C, McGwin G. Vision impairment and driving. Surv Ophthalmol. 1999;43(6):535-550. [doi:10.1016/s0039-6257(99)00035-1] [Medline: 10416796]

https://formative.jmir.org/2024/1/e58465

XSL• FO
RenderX

JMIR Form Res 2024 | vol. 8 | e58465 | p. 10(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
19.
20.
21.

22.
23.
24.
25.
26.
27.

28.

29.
30.

31.

32.
33.
34.
35.
36.

37.
38.

39.
40.
41.
42.
43.

Wood JM. Nighttime driving: visual, lighting and visibility challenges. Ophthalmic Physiol Opt. 2020;40(2):187-201. [doi:10.1111/opo.12659] [Medline: 31875993]
Wood J. Night driving: how low illumination affects driving and the challenges of simulation. In: Fisher DL, editor.
Handbook of Driving Simulation for Engineering, Medicine, and Psychology. Boca Raton, FL. CRC Press; 2011.
Wood J, Lacherez PF, Anstey KJ. Not all older adults have insight into their driving abilities: evidence from an on-roadassessment and implications for policy. J Gerontol A Biol Sci Med Sci. 2013;68(5):559-566. [doi: 10.1093/gerona/gls150][Medline: 22859387]
Jin C, Wu DZ, Wu L. The contrast sensitivity function in low vision. Yan Ke Xue Bao. 1992;8(1):45-48. [Medline: 1283738]
Pelli DG, Bex P. Measuring contrast sensitivity. Vision Res. 2013;90:10-14. [FREE Full text] [doi:10.1016/j.visres.2013.04.015] [Medline: 23643905]
Owsley C, Sloane ME. Contrast sensitivity, acuity, and the perception of 'real-world' targets. Br J Ophthalmol.
1987;71(10):791-796. [doi: 10.1136/bjo.71.10.791] [Medline: 3676151]
Chung S, Tjan BS. Spatial-frequency and contrast properties of reading in central and peripheral vision. J Vis.
2009;9(9):16-19. [FREE Full text] [doi: 10.1167/9.9.16] [Medline: 19761349]
Rae S, Latham K, Katsou MF. Meeting the UK driving vision standards with reduced contrast sensitivity. Eye (Lond).
2016;30(1):89-94. [FREE Full text] [doi: 10.1038/eye.2015.188] [Medline: 26471119]
Swan G, Shahin M, Albert J, Herrmann J, Bowers AR. The effects of simulated acuity and contrast sensitivity impairments
on detection of pedestrian hazards in a driving simulator. Transp Res Part F Traffic Psychol Behav. 2019;64:213-226.
[FREE Full text] [doi: 10.1016/j.trf.2019.05.003] [Medline: 31983886]
Owsley C, Swain T, Liu R, McGwin G, Kwon MY. Association of photopic and mesopic contrast sensitivity in older drivers
with risk of motor vehicle collision using naturalistic driving data. BMC Ophthalmol. 2020;20(1):47. [FREE Full text][doi: 10.1186/s12886-020-1331-7] [Medline: 32019520]
McGwin G, Chapman V, Owsley C. Visual risk factors for driving difficulty among older drivers. Accid Anal Prev.
2000;32(6):735-744. [doi: 10.1016/s0001-4575(99)00123-2] [Medline: 10994600]
Allen H, Beck KH, Zanjani F. Driving concerns among older adults: associations with driving skill, behaviors, andexperiences. Traffic Inj Prev. 2019;20(1):45-51. [FREE Full text] [doi: 10.1080/15389588.2018.1528358] [Medline:
30715919]
Subzwari S, Desapriya E, Babul-Wellar S, Pike I, Turcotte K, Rajabali F, et al. Vision screening of older drivers forpreventing road traffic injuries and fatalities. Cochrane Database Syst Rev. 2009;(1):CD006252. [doi:10.1002/14651858.CD006252.pub2] [Medline: 19160271]
American Medical Association. Physician's Guide to Assessing and Counseling Older Drivers, 2nd ed. Washington, DC.
National Highway Traffic Safety Administration; 2010.
The Ohio State University Center for Clinical and Translational Science. About StudySearch. 2023. URL: https://studysearch.
osumc.edu/ [accessed 2021-11-02]
ResearchMatch. Clinical Translational Science Award (CTSA) program. URL: https://www.researchmatch.org/ [accessed2021-11-02]
Lesmes L, Dorr M. Active learning for visual acuity testing. 2019. Presented at: Proceedings of the 2nd International
Conference on Applications of Intelligent Systems; 2019; New York, NY.
Zhao Y, Lesmes LA, Dorr M, Bex PJ, Lu ZL. Psychophysical validation of a novel active learning approach for measuring
the visual acuity behavioral function. Transl Vis Sci Technol. 2021;10(1):1. [FREE Full text] [doi: 10.1167/tvst.10.1.1][Medline: 33505768]
Lesmes L, Lu Zhong-Lin, Baek J, Albright TD. Bayesian adaptive estimation of the contrast sensitivity function: the quick
CSF method. J Vis. Mar 30, 2010;10(3):17.1-1721. [FREE Full text] [doi: 10.1167/10.3.17] [Medline: 20377294]
Hou F, Huang CB, Lesmes L, Feng LX, Tao L, Zhou YF, et al. qCSF in clinical application: efficient characterization andclassification of contrast sensitivity functions in amblyopia. Invest Ophthalmol Vis Sci. 2010;51(10):5365-5377. [FREEFull text] [doi: 10.1167/iovs.10-5468] [Medline: 20484592]
Xu P, Lesmes LA, Yu D, Lu Zhong-Lin. A novel Bayesian adaptive method for mapping the visual field. J Vis. Dec 02,
2019;19(14):16. [FREE Full text] [doi: 10.1167/19.14.16] [Medline: 31845976]
Hou F, Lesmes L, Bex P, Dorr M, Lu Zhong-Lin. Using 10AFC to further improve the efficiency of the quick CSF method.
J Vis. 2015;15(9):2. [FREE Full text] [doi: 10.1167/15.9.2] [Medline: 26161631]
Zhao Y, Lesmes LA, Dorr M, Bex P, Lu ZL. Accuracy and precision of the ETDRS chart, E-ETDRS and Bayesian qVA
method. Investig Ophthalmol Vis Sci. 2019;60(9):5908. [FREE Full text]
Zhao Y, Lesmes LA, Hou F, Lu Zhong-Lin. Hierarchical Bayesian modeling of contrast sensitivity functions in awithin-subject design. J Vis. Nov 01, 2021;21(12):9. [FREE Full text] [doi: 10.1167/jov.21.12.9] [Medline: 34792537]
Vingopoulos F, Wai KM, Katz R, Vavvas DG, Kim LA, Miller JB. Measuring the contrast sensitivity function in
non-neovascular and neovascular age-related macular degeneration: the quantitative contrast sensitivity function test. J
Clin Med. 2021;10(13):2768. [FREE Full text] [doi: 10.3390/jcm10132768] [Medline: 34202569]

https://formative.jmir.org/2024/1/e58465

XSL• FO
RenderX

Yang et al

JMIR Form Res 2024 | vol. 8 | e58465 | p. 11(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
44.

45.
46.

47.

48.
49.

50.

51.

52.
53.

54.

55.
56.

57.
58.

59.

60.

Yang et al

Stellmann JP, Young KL, Pöttgen J, Dorr M, Heesen C. Introducing a new method to assess vision: computer-adaptive
contrast-sensitivity testing predicts visual functioning better than charts in multiple sclerosis patients. Mult Scler J Exp
Transl Clin. 2015;1:2055217315596184. [FREE Full text] [doi: 10.1177/2055217315596184] [Medline: 28607699]
Schubert E. Light-emitting diodes. Cambridge, UK. Cambridge University Press; 2018.
Jones P, Ungewiss J, Eichinger P, Wörner M, Crabb DP, Schiefer U. Contrast sensitivity and night driving in older people:
quantifying the relationship between visual acuity, contrast sensitivity, and hazard detection distance in a night-time driving
simulator. Front Hum Neurosci. 2022;16:914459. [FREE Full text] [doi: 10.3389/fnhum.2022.914459] [Medline: 35966995]
Ungewiss J, Schiefer U, Eichinger P, Wörner M, Crabb DP, Jones PR. Does intraocular straylight predict night driving
visual performance? Correlations between straylight levels and contrast sensitivity, halo size, and hazard recognition distancewith and without glare. Front Hum Neurosci. 2022;16:910620. [FREE Full text] [doi: 10.3389/fnhum.2022.910620][Medline: 36177386]
Strayer DL, Turrill J, Cooper JM, Coleman JR, Medeiros-Ward N, Biondi F. Assessing Cognitive Distraction in the
Automobile. Hum Factors. Dec 2015;57(8):1300-1324. [doi: 10.1177/0018720815575149] [Medline: 26534847]
Nowosielski RJ, Trick LM, Toxopeus R. Good distractions: testing the effects of listening to an audiobook on driving
performance in simple and complex road environments. Accid Anal Prev. 2018;111:202-209. [doi: 10.1016/j.aap.2017.11.033][Medline: 29223795]
Jongen S, van der Sluiszen NNJJM, Brown D, Vuurman EFPM. Single- and dual-task performance during on-the-road
driving at a low and moderate dose of alcohol: a comparison between young novice and more experienced drivers. Hum
Psychopharmacol. 2018;33(3):e2661. [FREE Full text] [doi: 10.1002/hup.2661] [Medline: 29878502]
Stone BT, Correa KA, Brown TL, Spurgin AL, Stikic M, Johnson RR, et al. Behavioral and neurophysiological signaturesof benzodiazepine-related driving impairments. Front Psychol. 2015;6:1799. [FREE Full text] [doi:10.3389/fpsyg.2015.01799] [Medline: 26635697]
Brown T. A Systematic Review of Profiles of Speed and Lane Keeping for Driving Simulator Data. Washington, DC.
National Highway Traffic Safety Administration; 2023.
Verster JC, Roth T. Standard operation procedures for conducting the on-the-road driving test, and measurement of thestandard deviation of lateral position (SDLP). Int J Gen Med. 2011;4:359-371. [doi: 10.2147/IJGM.S19639] [Medline:
21625472]
Jahanjoo F, Sadeghi-Bazargani H, Hosseini ST, Golestani M, Rezaei M, Shahsavarinia K, et al. Exploring the causal impactof age and nighttime driving on road traffic injuries among elderly drivers: a bayesian LASSO approach. Bull Emerg
Trauma. 2023;11(3):125-131. [FREE Full text] [doi: 10.30476/BEAT.2023.98406.1427] [Medline: 37525652]
Plainis S, Murray IJ. Reaction times as an index of visual conspicuity when driving at night. Ophthalmic Physiol Opt. Sep
2002;22(5):409-415. [doi: 10.1046/j.1475-1313.2002.00076.x] [Medline: 12358311]
Sturgis SP, Osgood DJ. Effects of glare and background luminance on visual acuity and contrast sensitivity: implications
for driver night vision testing. Hum Factors. Jun 1982;24(3):347-360. [doi: 10.1177/001872088202400310] [Medline:
7129453]
Sturr JF, Kline GE, Taub HA. Performance of young and older drivers on a static acuity test under photopic and mesopic
luminance conditions. Hum Factors. 1990;32(1):1-8. [doi: 10.1177/001872089003200101] [Medline: 2376405]
Friedland H, Snycerski S, Palmer E, Laraway S. The effectiveness of glare-reducing glasses on simulated nighttime driving
performance in younger and older adults. Cogn Technol Work. Sep 30, 2017;19(4):571-586. [doi:10.1007/s10111-017-0442-2]
Vollset S, Goren E, Yuan Chun-Wei, Cao J, Smith AE, Hsiao T, et al. Fertility, mortality, migration, and population scenarios
for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. Lancet.
Oct 17, 2020;396(10258):1285-1306. [FREE Full text] [doi: 10.1016/S0140-6736(20)30677-2] [Medline: 32679112]
Mollenkopf H. The role of driving in maintaining mobility in later life: a European view. Gerontechnology.
2002;2(1):231-250. [doi: 10.4017/GT.2002.01.04.003.00]

Abbreviations
AULCSF: area under the log contrast sensitivity function
CSF: contrast sensitivity function
OSU: Ohio State University
SDSpeed: SD of speed
SDLP: SD of lane position
VA: visual acuity
VFM: visual field map
VUSVFM: volume under the surface of visual field map
qCSF: quantitative contrast sensitivity function test
qVFM: quantitative visual field map test

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Edited by A Mavragani; submitted 15.03.24; peer-reviewed by A Colenbrander, M Mahjoob; comments to author 16.04.24; revised
version received 04.05.24; accepted 04.05.24; published 26.06.24
Please cite as:
Yang J, Alshaikh E, Yu D, Kerwin T, Rundus C, Zhang F, Wrabel CG, Perry L, Lu Zhong-Lin
Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an
Observational StudyJMIR Form Res 2024;8:e58465
URL: https://formative.jmir.org/2024/1/e58465
doi: 10.2196/58465
PMID: 38922681©Jingzhen Yang, Enas Alshaikh, Deyue Yu, Thomas Kerwin, Christopher Rundus, Fangda Zhang, Cameron G Wrabel, Landon
Perry, Zhong-Lin Lu. Originally published in JMIR Formative Research (https://formative.jmir.org), 26.06.2024. This is an
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