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RESEARCH
Smartphone use as a possible risk factor for myopia
Clin Exp Optom 2020 DOI:10.1111/cxo.13092
Saoirse McCrann* PhD BSc
James Loughman*
†
PhD BSc
John S Butler
‡
PhD MSc BA
Nabin Paudel* PhD BOptom
Daniel Ian Flitcroft*
§
DPhil MB BS MA
FRCOphth
*Centre for Eye Research Ireland, School of Physics,
Clinical and Optometric Sciences, Technological
University Dublin, Dublin, Ireland
†
Department of Optometry, African Vision Research
Institute, University of KwaZulu Natal, Durban,
South Africa
‡
School of Mathematical Sciences, Technological
University Dublin, Dublin, Ireland
§
Department of Ophthalmology, Children’s University
Hospital, Dublin, Ireland
E-mail: saoirse.mccrann@tudublin.ie
Submitted: 4 February 2020
Revised: 27 April 2020
Accepted for publication: 27 April 2020
Clinical relevance: This study demonstrates an association between myopia and
smartphone data usage. Youths now spend more time participating in near tasks as a result
of smartphone usage. This poses an additional risk factor for myopia development/progres-
sion and is an important research question in relation to potential myopia management
strategies.
Background: Children are now exposed to another possible environmental risk factor for
myopia –smartphones. This study investigates the amount of time students spend on their
smartphones and their patterns of smartphone usage from a myopia perspective.
Methods: Primary, secondary and tertiary level students completed a questionnaire explor-
ing patterns of smartphone usage and assessing their attitudes toward potential myopia
risk factors. Device-recorded data usage over an extended period was quantified as the pri-
mary and objective indicator of phone use. Average daily time spent using a smartphone
was also quantified by self-reported estimates. Refractive status was verified by an
optometrist.
Results: Smartphone ownership among the 418 students invited to participate was over
99 per cent. Average daily smartphone data and time usage was 800.37 1,299.88 MB and
265.16 168.02 minutes respectively. Myopic students used almost double the amount of
smartphone data at 1,130.71 1,748.14 MB per day compared to non-myopes at
613.63 902.15 MB (p = 0.001). Smartphone time usage was not significantly different
(p = 0.09, 12 per cent higher among myopes). Multinomial logistic regression revealed that
myopic refractive error was statistically significantly associated with increasing daily
smartphone data usage (odds ratio 1.08, 95% CI 1.03–1.14) as well as increasing age (odds
ratio 1.09, 95% CI 1.02–1.17) and number of myopic parents (odds ratio 1.55, 95% CI
1.06–2.3). Seventy-three per cent of students believed that digital technology may adversely
affect their eyes.
Conclusion: This study demonstrates an association between myopia and smartphone data
usage. Given the serious nature of the ocular health risks associated with myopia, our find-
ings indicate that this relationship merits more detailed investigation.
Key words: lifestyle, myopia, myopia prevention, risk factors, smartphones
Myopia is predicted to affect almost five bil-
lion people worldwide by 2050,
1
and is a
global public health concern with significant
social, educational, and economic conse-
quences.
2
The onset of myopia has also
shifted to a younger age,
3
which is a con-
cern, as younger children exhibit more rapid
myopia progression
4
and are more likely to
reach higher levels of myopia. This can sub-
stantially increase the risk of developing
sight-threatening conditions including myo-
pic maculopathy, glaucoma, cataract and
retinal detachment in later life.
5
The aetiology of myopia is multifacto-
rial, involving interplay between genetic
environmental and behavioural factors,
with decreased time outdoors,
6
urbanisation,
7
disturbed/delayed sleep,
8,9
increased time
spent in education
10
and time spent reading
continuously or in long periods of close work
all cited as possible influences.
11
Children and young adults are now
exposed to another possible environmental
risk factor for myopia –digital devices.
12
Smartphones, iPads, tablets and computers
are used at a very early age in both home
and school environments.
13
Children are the
fastest growing population of smartphone
users,
14
with 95 per cent of American teen-
agers reporting ownership of or access to a
smartphone in 2018.
15
Smartphones are
now the most used device for internet access
on a daily basis by 9–16-year-olds in
Ireland,
16
while 85 per cent of young people
in the UK (aged 12–15) use a smartphone
daily.
17
Several studies have identified computer
usage as a risk factor for myopia.
18–23
One
study in particular, found myopia was asso-
ciated with a closer computer screen work-
ing distance.
20
The working distance
adopted by smartphone users is typically
even closer than for computer screens.
24
It
is conceivable, therefore, that increased and
continuous exposure to a smartphone
screen might represent a plausible risk fac-
tor for the development or progression of
myopia, especially in younger age groups.
© 2020 Optometry Australia Clinical and Experimental Optometry 2020
1
CLINICAL AND EXPERIMENTAL
However, there is a scarcity of published
literature investigating the relationship
between smartphone use and myopia.
Recent studies that have addressed the ocu-
lar impact of smartphone use have focused
on self-reported estimates of time spent on a
smartphone,
25–28
even though self-reported
smartphone assessments have been shown
to perform poorly when attempting to predict
objective smartphone behaviours.
29
This study was designed to investigate self-
reported and device-tracked smartphone usage
among children and young adults to determine
whether any association exists with refractive
status. Furthermore, the attitudes of students to
mobile phones and digital technology as a risk
factor for myopia were also explored.
Methods
Participants
Students across the spectrum of primary
school (kindergarten to grade 6), secondary
school (corresponding to grades 7–12) and
tertiary (or university level) education settings
were invited to participate in the study
between January and March 2018. This was
facilitated by a n ‘invitation to participate’email
request sent to university staff via university
administrators and to schools in the Republic
of Ireland directly by the study investigator.
The study investigator visited participating
classrooms and potential participants were
provided with a questionnaire. The study
investigator explained the instructions on the
questionnaire carefully with each class, and
any questions were answered. For partici-
pants aged 16 and over, a consent form was
signed and the questionnaires were com-
pleted instantly and collected by the study
investigator. Students under the age of 16 and
any subject over 16 who did not have their
phone present in the classroom completed
the questionnaire for homework along with
the parental consent form (where applicable),
and returned it to their teacher the following
day. Completed forms were collected one
week after distribution. Schools were con-
tacted the day before the study investigator’s
return, to remind students to return their
questionnaires if they had not done so. All stu-
dents present on the day of the initial investi-
gator visit agreed to participate in the study.
Study conduct
As the study was performed in a classroom
rather than a clinical setting, a simple
optometrist-led method was used to separate
myopes from non-myopes. Prior to the study
investigator visit, participants (or parents)
were requested to bring a copy or photograph
of their glasses or contact lens prescription to
school, which was documented by the investi-
gator. The investigator, a qualified optome-
trist, confirmed refractive status (including for
those without a written prescription) by
questioning student’s use of their spectacle/
contact lens prescription, their unaided signs
and symptoms and by examining the stu-
dents’spectacles to determine if lenses were
convex (magnifying and hence hyperopic) or
concave (minifying and hence myopic).
Questionnaire
An initial draft questionnaire was con-
structed and subsequently analysed by an
external reviewer with expertise in question-
naire design. The questionnaire was pilot-
tested on five people (two primary school
students, two secondary school students
and one university student), after which it
was edited to remove leading or confusing
questions. For Android users, smartphone
data usage was queried by going into Set-
tings > Data Usage > Mobile Data Usage as
well as Settings > Data Usage > WI-FI Data
Usage. For iPhone users, smartphone data
usage was found via Settings > Mobile Data
> Data Usage in Current Period, as well as
Settings > Mobile Data > WI-FI Data Usage.
Participants were asked to record the time
period for data usage based on their current
usage period (for Android users) or date of
last reset (for iPhone users). These values
are available within the phone settings and
indicate the date from which the phone has
been logging cellular data usage.
Average daily data usage was calculated
by dividing the number of days from the last
data reset by the amount of data used. Stu-
dents were also asked to record the three
applications (apps) that used the most data.
Smartphone usage was also assessed by
self-report. Participants were asked to esti-
mate how much time they spend on aver-
age per day using their phone, the longest
period of time spent on their phone at any
one period in a week and how long they
spend looking at their phone after going to
bed. Nine tick box questions were used to
capture participant demographics, record
participant and self-reported parental myo-
pia status, explore patterns of smartphone
use (for example, whether used to read or
watch TV programs, use for social media,
internet and so on), quantify how often the
phone was used after going to bed and to
determine if participants thought the use of
a phone screen impacted their eyes. An
open-ended question probed participants’
thoughts on the potential impact of the
screen on their eyes. Parents were asked to
assist in answering the questionnaire for
participants under 16 years old.
Data analysis
Questionnaires were anonymous; partici-
pants were assured that all individual
results would be kept strictly confidential.
Participation in the study was voluntary. The
study was approved by the Research Ethics
Committee at Technological University Dub-
lin. All data was collected between January
and March 2018. The data collected was
analysed on the statistical package for social
sciences (IBM SPSS Statistics for Windows,
Version 22.0; IBM, Armonk, NY, USA) and R
version 3.2.2.in RStudio (RStudio, Inc., Bos-
ton, MA, USA). The Kolmogorov–Smirnov
test for normality determined the
smartphone usage data was not normally
distributed. A Box–Cox transformation was
therefore used to normalise smartphone
data usage and time usage to facilitate para-
metric analysis. Non-parametric tests were
used and the median and confidence
intervals were reported where appropriate.
The results were analysed using descriptive
statistics and inferential statistics including
Spearman’s rank order correlation, chi-
squared tests of independence, Kruskal–Wallis
and Mann–Whitney U-tests. A statistical signifi-
cance level of p < 0.05 was adopted through-
out the analysis.
Results
Demographics
Three of the 418 (< one per cent) students
initially invited to participate in the study did
not own a smartphone (but used their par-
ent’s smartphone) and were excluded as
their personal data usage could not be iden-
tified. Four hundred and two participants
(96 per cent) aged between 10–33 years pro-
vided informed consent and completed the
questionnaire (54 per cent, 216/402 female;
45 per cent, 181/397 male; one per cent,
5/402 not stated). The mean age was 16.77
(standard deviation [or ] 4.4) years and
34 per cent (138/402) of participants wore
glasses/contact lenses for myopia. The mean
age at which myopic participants were first
prescribed glasses was 11 years (range
3–19). There was some minor loss of data on
Clinical and Experimental Optometry 2020 © 2020 Optometry Australia
2
Smartphones as a possible risk factor for myopia McCrann, Loughman, Butler et al.
specific questions due to incomplete
responses or inability to confirm refractive
status (spectacles or spectacle/contact lens
prescription not provided by six participants).
A detailed description of the recruitment set-
ting, data capture and refractive status con-
firmation of all participants is provided in
Figure 1, while participant demographics,
behaviours and beliefs according to refrac-
tive status are provided in Table 1.
Smartphone usage
Students used an average of
873 1,038 MB of data per day and spent
an average of four hours and
32 169 minutes per day on their phone.
The longest period students reported spend-
ing on their phone at any one period in a
week was an average of three hours
28 188 minutes. The mean period since
smartphone data was last reset was
215 320 days. Data usage among myopic
students was statistically significantly higher
(84 per cent higher, p = 0.001) than non-
myopes (Table 1). Self-reported smartphone
time usage was not statistically significantly
(p = 0.09) different between myopes and
non-myopes (12 per cent higher self-
reported use among myopes, Table 1).
Spearman’s correlation revealed daily data
usage (r = 0.14, df = 311, p = 0.01) and daily
time spent on a smartphone (r = 0.04,
df = 311, p = 0.41) was positively correlated
with age. Simple linear regression analysis
was used to test the relationship between
Box–Cox normalised daily data usage and
daily time spent on the phone. The results of
the regression indicated three per cent of
the variance could be explained by the
model (daily data usage versus daily time)
(R
2
=0.033,F
[1,302]
= 10.2, p < 0.002).
The variation of data usage and time
spent on a phone as a function of
age/educational level is shown in Figure 2.
The distribution of smartphone usage, par-
ticularly data usage, was positively skewed
in both refractive groups. Non-parametric
analysis (Mann–Whitney U-test) for each
educational level showed a significant dif-
ference in daily data usage between myo-
pic and non-myopic university students
(p = 0.02) and a significant difference in
daily time on phone between myopic and
non-myopic primary school students
(p = 0.02). Other comparisons were not sig-
nificant. Log transformation of the usage
data still resulted in a small amount of
negative skew, as shown in the box-and-
whisker plots in Figure 2. Subsequent para-
metric analysis on smartphone data usage
was therefore performed following
normalisation using a Box–Cox trans
formation.
Eighty-four per cent (342/406) of students
reported using their phone in bed. Spearman’s
correlation revealed age and time spent on a
phone in bed were inversely correlated (ρ
[323] = −0.25, p = 0.0001), with younger partici-
pants spending more time on a smartphone
in bed compared to older students.
For most participants (72 per cent; 301/418),
the main purpose of their smartphone was to
use social media apps that involve screen
interaction. Snapchat, Instagram and Facebook
were the most used apps across all age
groups and refractive error profiles. Spotify,
podcasts and music applications that require
less visual interaction by users were the most
used applications by only four participants in
the study.
Figure 1. Participant recruitment, data capture and refractive status confirmation
flowchart
© 2020 Optometry Australia Clinical and Experimental Optometry 2020
3
Smartphones as a possible risk factor for myopia McCrann, Loughman, Butler et al.
Parent myopia status
Myopic participants with one (p = 0.01) and
two (p = 0.04) myopic parents were first pre-
scribed glasses for myopia at a younger age
compared to myopic participants with no
parental history of myopia.
Gender
A chi-squared test of independence revealed
myopia status was not statistically significantly
dependent on gender (χ
2
[1] = 3.57, p = 0.06).
Beliefs regarding digital
technology and eye health
Overall 73 per cent (296/406) of students
believed that digital technology may adversely
affect their eyes, which was inversely corre-
lated with age (ρ[402] = −0.15, p = 0.003). This
belief was expressed statistically significantly
more often by myopes (84 per cent; 112/134)
than non-myopes (68 per cent; 175/259)
(p = 0.001). Participants regarded screen
usageasacauseofvarioussymptoms
including eye strain (29 per cent; 111/386),
dry eyes (67 per cent; 28/386), headaches (five
per cent; 18/385), and difficulty reading (two
per cent; 9/383). A similar proportion of
myopes (31 per cent; 39/127) and non-
myopes (25 per cent; 61/246) expressed an
opinion that a link existed between myopia
and increased time spent looking at a
screen(p=0.22).
The above factors (that is, refractive sta-
tus, phone usage, age, gender, number of
myopic parents, and beliefs) were incorpo-
rated into a multinomial logistic regression
model and revealed that myopic refractive
error status was statistically significantly
associated with increasing Box–Cox trans-
formed daily smartphone data usage
(p = 0.002), as well as increasing age (p = 0.01)
and number of myopic parents (p = 0.008)
(Table 2). A similar multinomial logistic regres-
sion revealed that myopic refractive error sta-
tus was statistically significantly associated
with Box–Cox transformed daily time spent
on mobile phones (p = 0.04) as well as
increasing age (p < 0.001), and number of
myopic parents (p = 0.03, Table 3).
Discussion
This study found an association between
increased smartphone data usage and myo-
pia with myopic participants using almost
double the amount of data on a daily basis
Figure 2. Daily smartphone data usage (MB) and daily self-reported smartphone usage
time (minutes) according to education level for both myopic and non-myopic participants.
Asignificant difference was found in daily data usage between myopic and non-myopic uni-
versity students (p = 0.02, Mann–Whitney U-test) and in daily time on phone between myo-
pic and non-myopic primary school students (p = 0.02, Mann–Whitney U-test).
Myopes Non-myopes p-value
Demographics
Age (mean) 18 4 (9.33) 16 5 (10.40) 0.002
Male 38% (48/128) 48% (124/257) 0.058
†
Proportion of myopic parents
No myopic parents 40% (55/137) 56% (143/257) 0.11
†
One myopic parent 45% (61/137) 36% (92/257)
Two myopic parents 15% (21/137) 9% (24/257)
Smartphone behaviour
Data usage per day (MB) 1,131 1,748 (0.36–10,534) 614 902 (0–6,000) 0.001
Time on phone per day (minutes)
§
288 174 (10–1,080) 258 163 (5–785) 0.09
Phone in bed every night 64% (86/134) 61% (159/259) 0.72
‡
Usage time in bed (minutes) 67 68 (0–455) 71 104 (1–1,335) 0.65
Smartphone-related beliefs
Belief screens may affect eyes 84% (112/134) 68% (175/259) 0.001
Belief screens may cause myopia 31% (19/127) 25% (61/246) 0.223
Bold value indicates statistically significant results. Results indicated as mean standard deviation (range). p-values calculated using
the Mann–Whitney U-test or, where otherwise indicated, using chi-squared (†) and Kruskal–Wallis H (‡) tests.
§
Self-reported.
Table 1. Participant demographics, smartphone behaviour and related beliefs according to refractive status
Clinical and Experimental Optometry 2020 © 2020 Optometry Australia
4
Smartphones as a possible risk factor for myopia McCrann, Loughman, Butler et al.
compared to those without myopia. This
association remained significant even after
statistical correction for possible con-
founders such as variation in data usage
with age, number of myopic parents, gender
and beliefs regarding technology that may
influence smartphone usage patterns.
The lifestyle habits of children and teen-
agers today have undeniably changed with
advancements in technology and while the
prevalence of myopia has been increasing
for decades, the increased level of near
visual stimulation from smartphones may
pose an additional independent risk for
myopia. Smartphones differ from traditional
reading in various aspects such as wave-
length, distance from the eye, size, contrast,
resolution, temporal properties and spectral
composition, all of which merit investigation.
Aside from this, children and adolescents now
spend more than ever using a smartphone
that demands proximal attention, which may
compete with other more protective activities
such as time outdoors.
6,13
Thetime(self-
reported) devoted by children to smartphone
use alone in the current study, excluding all
other proximal tasks, is close to double that
observed for all near work activities outside
school hours in a study from Singapore
(four hours 32 minutes compared to two hours
42 minutes per day)
30
andinaUSstudy
(four hours 32 minutes compared to two hours
18 minutes).
31
Moreover, smartphone owner-
ship has increased dramatically among youn-
ger age groups in both advanced and emerging
economies,
32
with over 99 per cent of students
in the current study owning a smartphone and
younger participants spending more time on a
smartphone in bed compared to older stu-
dents. Our findings indicate that children and
adolescents are now spending substantially
more time focusing on proximal tasks com-
pared to that observed in studies conducted in
the early and pre-smartphone era.
11,31
In 2001, before the advent of
smartphones, Saw et al. reported myopic
children spent 40 minutes more than non-
myopic participants participating in total
near work activities daily.
30
Mutti et al. also
reported myopes spent an additional
42 minutes per day on the computer, study-
ing and reading compared to non-myopes.
31
This is similar to the additional 32 minutes
spent by myopes using their smartphones
compared to non-myopes reported herein.
However, there is an apparent discordance
in the level of data and time usage differ-
ences observed between myopes and non-
myopes. It is highly unlikely that the large
data disparity is accurately reflected in the
relatively small time difference found using
the self-reported measure. Although statisti-
cally significant, the correlation between
data usage and self-reported usage time in
this study was weak, which possibly indi-
cates low criterion validity for self-reported
measures.
33
There is evidence to suggest
that self-reported measures of smartphone
use are typically underestimated and not
reliable indicators of actual use.
34
Records
of data usage, as collected herein, provide
an objective, quantifiable and verifiable
measure of phone use over an extended
period of time, yielding a better indicator of
smartphone behaviour than self-reported
usage data. Furthermore, there is no vali-
dated questionnaire developed to assess
subjective near work or smartphone usage,
which is a limitation of any study that relies
on self-reported data. Therefore the use of
smartphone data as a surrogate indicator of
phone use provides a better indicator of
smartphone behaviour than self-reported
usage data.
29
The extended period of data
usage evaluated is particularly important in
that it limits the possible influence of theo-
retical confounders such as time of week
Independent variable B SE(B) z-value Prob Odds Odds confidence intervals
Box–Cox daily time usage 0.02585 0.01241 2.084 0.0372 1.026 (1.001–1.051)
Age 0.13115 0.03069 4.273 < 0.001 1.14 (1.076–1.21)
Number of myopic parents 0.39767 0.17823 2.231 0.025 1.488 (1.05–2.116)
Technology beliefs
†
0.53595 0.29441 1.820 0.0687 1.709 (0.97–3.092)
Gender −0.4620 9.25381 −0.182 0.856 0.954 (0.579–1.57)
†
Technology beliefs = belief that technology negatively impacts eyes.
Table 3. Summary of logistic regression analysis for variables predicting myopic status by Box–Cox of daily time spent on a
smartphone (minutes), age, parental myopia, a belief that technology can negatively impact eyes and gender for n = 364
Independent variable B SE(B) z-value Prob Odds Odds confidence intervals
Box–Cox daily data usage 0.08068 0.02583 3.123 0.002 1.08 (1.031–1.142)
Age 0.08708 0.03541 2.460 0.014 1.09 (1.02–1.17)
Number of myopic parents 0.44240 0.19709 2.245 0.008 1.55 (1.06–2.301)
Technology beliefs
†
0.4448 0.31001 1.434 0.151 1.55 (1.001–3.301)
Gender 0.10949 0.28154 0.389 0.697 1.12 (0.644–1.94)
†
Technology beliefs = belief that technology negatively impacts eyes.
Table 2. Summary of logistic regression analysis for variables predicting myopic status by Box–Cox of daily data usage (MB), age,
parental myopia, a belief that technology can negatively impact eyes and gender for n = 286
© 2020 Optometry Australia Clinical and Experimental Optometry 2020
5
Smartphones as a possible risk factor for myopia McCrann, Loughman, Butler et al.
(weekday versus weekend). Additionally the
data is likely more reflective of typical daily
life and not limited to short-term recall
which would influence self-reported time
usage estimates.
Although gender-based differences in
myopia prevalence in children have been
identified in certain populations,
35
gender
was not statistically significantly associated
with myopia status in this study, which is in
agreement with observations in the North-
ern Ireland Childhood Errors of Refraction
(NICER) study in Northern Ireland. Percep-
tions relating to the possible ocular effects
of smartphones were also explored as a
means to elucidate the impact, if any, of
such beliefs on the habitual usage of such
devices. Our findings suggest that believing
phone usage is deleterious to eye health
does not limit use. This belief was expressed
more often among myopes, in whom
smartphone use was greatest.
A range of factors could be associated with
the onset and/or progression of myopia in
smartphone use which merit further investi-
gation. These include excessive accommoda-
tion or closer working demands,
10,31,36
higher
accommodative convergence/accommodation
ratios,
37,38
and peripheral defocus.
5,39,40
Fur-
thermore, bedtime mobile phone use can dis-
turb and delay sleep,
41–43
and future research
should continue to investigate associations
between myopia and circadian rhythm, lack
of sleep and poor sleep quality.
8,9,44
Limitations of the study
The results of this study are limited in that
the case control design limits any causal
inferences regarding the observed associa-
tion between smartphone use and myopia.
Future studies should seek to address cau-
sality through prospective design. However,
the study represents a large study sample
of smartphone users across the entire edu-
cation level and age spectrum during which
myopia development and progression is
most likely,
45
and thus, the period during
which environmental influences may pose a
significant risk to the development of myo-
pic refractive error.
One consideration is how much of the
data usage relates to visual tasks. This study
predates the built-in ‘screen time’app of iOS
12 that provides daily and weekly activity
reports of the total time a person spends in
each app they use.
46
Background programs
as well as some apps (for example, apps
which download files and videos or high-
resolution video streaming apps such as
YouTube and Netflix) use more data so
smartphone data consumption does not
necessarily correlate with time spent looking
at a smartphone;
47
however, it is likely that
any influence of such factors is balanced
across the two study groups. It has also
been demonstrated that the use of social
networking apps account for the majority of
active time spent on a smartphone and
corresponding data traffic.
48
Interaction
with these social media apps requires a high
level of visual participation. Additionally,
applications that play music and therefore
do not require a person to look at a screen
were not in the top applications that used
most data in this study.
As the study was performed in a class-
room rather than a clinical setting, a formal
eye examination was not conducted as part
of the study. However, a qualified optome-
trist carefully reviewed every participant
who reported spectacle/contact lens use in
order to determine their refractive status.
This method is more robust than self-
classification of myopia status which has
been performed in a range of studies. Self-
classification of myopia has been found to
be reasonably reliable and provides lower-
bound to any potential underestimation.
49
The possibility that some children may have
had uncorrected refractive error may have
led to an underestimation of the number of
myopes. As a validation, the proportion of
myopes in this study attending primary
(< 13 years) and secondary school (13–18 years)
was 15 and 26 per cent, respectively; compa-
rable to the prevalence of myopia in
schoolchildren reported in the recent Ireland
Eye Study (12–13 years, 19.9 per cent) and to
the UK NICER study (12–13 years, 16.4 per
cent, 18–20 years, 18.6 per cent), so any
underestimation is likely minimal.
3
The confir-
mation of the association between myopic
parents and myopia in their children also
affirms the validity of the myopic classification
procedure.
Time spent outdoors was not recorded in
the study and extensive screen time may
influence time spent participating in out-
door activities, although mobile phone use
is not limited to indoors or outdoors.
Although we cannot be definitive as to
whether more smartphone usage equates
to less time outdoors, it is highly likely that
the levels of daily usage reported herein
would certainly compete with and limit the
time available to children and adolescents
for outdoors-based activities. Future studies
should incorporate objective measures of
light and outdoors exposure patterns to
address this issue more comprehensively.
50
Conclusion
The escalating prevalence of myopia is not a
recent phenomenon and certainly predates
smartphones, but the current generation of
children are the first to grow up in an era of
smartphone dependency. This study dem-
onstrates an association between myopia
and smartphone data usage. Children are
now spending substantially more time
focusing on proximal tasks compared to
that observed in studies conducted in the
pre-smartphone era, posing an additional
environmental risk factor for myopia. Given
the serious nature of the ocular health risks
associated with myopia, our findings indi-
cate that this relationship merits more
detailed investigation.
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