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Myopia has reached epidemic proportions around the world. China has one of the highest incidence rates of myopia and is the country with the most severe epidemic of myopia worldwide.1,2 Previous research concerning environmental factors that influence myopia has focused mainly on near-viewing and outdoor activities. Basic research,3 animal studies,4 cross-sectional studies,5 longitudinal studies,6,7 and randomized controlled trials8,9 have confirmed that longer near-viewing time and less outdoor time are risk factors associated with myopia.8,10,11 With increased learning pressure on children, many online tutoring classes have begun to be offered, which rely on digital devices like computers and smartphones. The correlation between digital screens and myopia progression is controversial.12–15–15 The most intuitive assumptions are that increased digital screen time could increase myopia because of increased near-viewing time16; however, there is currently insufficient evidence to support this.7
The outbreak of coronavirus disease 2019 (COVID-19) was declared a global pandemic by the World Health Organization in March 2020.17 To cut off disease transmission and reduce the risk of infections, students in China were required to take school classes or tutoring courses online from February to early May 2020.18 During this special period, the learning mode differed from the traditional mode of schooling. In the traditional school mode, students need to complete seven classes every day, with each class lasting 40 minutes, and they will take a 10-minute rest between each class, with 2½ hours of rest and extra physical education class every day. They do not use computers at school. However, digital screens are widely used in online classes, which may influence myopia progression.
To objectively record the visual behaviors of children with myopia, wearable devices were used from November 2019 to November 2020 in our study, which included both periods with learning provided in online class mode and traditional school mode. We aimed to explore the effect of the learning mode on myopia progression among children aged 9 to 11 years in Chongqing, China, by analyzing visual behaviors during the aforementioned two periods as well as changes in ocular biometric parameters for 1 year.
METHODS
Participants
Over the course of this study, a total of 49 children were included. Study participants were recruited from the population of children who attended the Ophthalmology Department at Yongchuan Hospital of Chongqing Medical University, which is in western Chongqing, China. To ensure consistency among influencing factors, participants' age was limited to 9 to 11 years. The sample size of this study refers to previous studies19–21–21 and previous epidemiological data in Yongchuan.22 It was calculated from previous epidemiological studies of myopia in this region, and the power analysis was completed. This research was reviewed by an independent ethical review board and conforms with the principles and applicable guidelines for the protection of human subjects in biomedical research. This study was conducted in accordance with the Helsinki Declaration and was approved by the ethics committee of our hospital. The study is registered in the Chinese Clinical Trial Register (registration time: June 19, 2018; registration number: ChiCTR1800016708; http://www.chictr.org/ [China]). All participants and their parents signed an informed consent form.
All participants had been included by November 2019. The inclusion criteria were (1) age 9 to 11 years, and (2) equivalent spherical refraction <−0.50 and ≥−4.00 D and astigmatism ≤1.00 D. The exclusion criteria were (1) one or both parents with a high myopia (equivalent spherical refraction ≥−6.00 D), (2) presence of ocular disease, (3) history of ocular surgery or trauma, and (4) use of other means of myopia prevention and control, such as wearing orthokeratology lenses.
Device
Study participants were required to wear Akeso eye care glasses (Akeso note 2S; Beijing Akeso Technology Co., Ltd., Beijing, China) from November 2019 to November 2020. The device has sensor chips installed in the right spectacle arm, located on the same horizontal line as the line of sight. This device contains a built-in six-axis sensor, ultraviolet (UV) light sensor (UV ≥1 or illuminance ≥1500 lux is recognized as outdoor light level; UV <1 and illuminance <1500 lux is recognized as indoor light level), and a proximity sensor, to record glasses-wearing time, outdoor time, and near-viewing time in real time and to store historical data every 3 minutes. Parents could connect to the smart device using a mobile phone and upload the stored historical data to the cloud. We provided an antislip sleeve to ensure the correct position of the glasses when worn. All devices underwent complete quality control before delivery.
Ophthalmologic Examination
All participants had undergone an eye examination, including an assessment of eye health, corneal curvature radius, axial length, and cycloplegic autorefraction at baseline (November 2019), 6 months (May 2020), and 1 year (November 2020).
The axial length was measured using an optical interferometric axial length measuring instrument (AL-scan; NIDEK, Tokyo, Japan) six or more times, and the average value was recorded. Full cycloplegia was obtained 45 minutes after administration of 1% cyclopentolate eye drops. Objective optometry was measured with an autorefractometer (AR-1; NIDEK) three or more times, and the average value was recorded.
Data Collection
Except for eating, sleeping, and bathing, participants were required to wear the glasses continuously for most of their study time and daily living activities during the day (8 hours or more). Participants' parents were required to upload data and charge the device every 2 days. Doctors contacted the parents to ensure that the glasses were functioning normally every week. Participants were required to wear the Akeso glasses for 1 year. The COVID-19 outbreak and its subsequent pandemic occurred during the current study period.
Data regarding glasses-wearing time, outdoor time, and near-viewing time were collected. All data were downloaded from the cloud platform, which included online class mode (March 2 to 15, 2020, during the COVID-19 pandemic) and traditional school mode (after school attendance had resumed, from May 20 to June 2, 2020). Data were collected from 7:00 am to 10:00 pm every day for 2 weeks in each mode, and nearly all learning time was included. To ensure efficient recording time and reduce experimental errors, the glasses-wearing time was used as a criterion for filtering out data in any day that comprised a period less than 8 hours; 8:00 am to 12:00 am and 2:00 pm to 6:00 pm were defined as study hours.
Statistical Analysis
Statistical analyses were performed using IBM SPSS version 26.0 (IBM Corp., Armonk, NY). Quantitative variables with a normal distribution are presented as mean ± standard deviation. The Mann-Whitney U test was used for nonnormally distributed data, and the variables are expressed as median (interquartile range [IQR]). An independent-samples t test was used for the comparison of two independent samples. Correlation was analyzed using the Spearman correlation. A P value of <.05 was defined as statistically significant.
RESULTS
A total of 49 participants with a mean ± standard deviation age of 10.88 ± 0.85 years were included in this study, comprising 26 boys (53.1%) and 23 girls (46.9%). The average glasses-wearing time was 752.02 ± 107.06 minutes.
Outdoor Activity
As shown in Table 1, the daily outdoor time throughout the day was significantly lower in online class learning mode (median, 9.5 minutes; IQR, 0.75 to 48 minutes) than school mode (median, 29 minutes; IQR, 11.50 to 50 minutes; P < .001).
TABLE 1 -
Comparison of outdoor time (in minutes per day)
|
Online class mode |
School mode |
P
|
| All day |
9.5 (0.75–48) |
29 (11.50–50) |
<.001* |
| Weekday |
6 (0–37.50) |
32 (13.50–50) |
<.001* |
| Weekend |
20 (1–95.50) |
20.5 (4.25–51.75) |
.59 |
Values in the table represent median (interquartile range). *P < .001.
The temporal patterns of outdoor time are shown in Fig. 1. On weekdays, participants clearly spent more time outdoors in school mode (median, 32 minutes; IQR, 13.50 to 50 minutes) than in online class mode (median, 6 minutes; IQR, 0 to 37.50 minutes; P < .001). Less time was spent outdoors in online class mode than in school mode during both study hours and nonstudy hours. Outdoor time showed no difference between online class mode (median, 20 minutes; IQR, 1 to 95.50 minutes) and traditional school mode (median, 20.5 minutes; IQR, 4.25 to 51.75 minutes) on the weekends (P = .59). The average outdoor time between 1:00 pm and 2:00 pm on weekdays in online class mode was significantly longer than in school mode (P < .001).
FIGURE 1: Temporal patterns of average hourly outdoor time. (A) On weekdays, more time was spent outdoors during study hours in school mode than in online class mode. (B) More time was spent outdoors in online class mode than in school mode on weekends. (C) There was no significant difference between the two modes on all days.
Near-viewing Time
As shown in Table 2, the average daily near-viewing time in online class mode (396.58 ± 114.41 minutes) was significantly greater than in school mode (376.52 ± 93.99 minutes; P = .007, F = 19.56).
TABLE 2 -
Compare of near-viewing time (in minutes per day)
|
Online class mode |
School mode |
P
|
F
|
| All day |
396.58 ± 114.41 |
376.52 ± 93.99 |
.007* |
19.56 |
| Weekday |
418.98 ± 108.13 |
388.39 ± 85.50 |
<.001** |
19.31 |
| Weekend |
344.57 ± 112.11 |
345.52 ± 107.55 |
.95 |
0.90 |
Values in the table represent mean ± standard deviation. *P < .01. **P < .001.
The temporal patterns of near-viewing time are shown in Fig. 2. There were only significant differences between the two modes on weekdays (P < .001, F = 19.31). The average hourly near-viewing time in online class mode was greater than that in school mode during study hours (all P < .001). On weekends, there were no significant differences.
FIGURE 2: Temporal patterns of average hourly near-viewing time. (A) Average hourly near-viewing time in online class mode was greater than that in school mode on weekdays. (B) There were no significant differences between the two modes on weekends. (C) Compared with online mode, children spent less time on near work in school mode on all days.
Equivalent Spherical Diopter and Axial Length
Compared with the baseline (November 2019) examination (−2.33 ± 0.81 D), the average spherical equivalent refraction corresponding to the examination in May 2020 (−2.94 ± 0.83 D, P = .001) was decreased in oculus dexter, indicating a significant increase in myopia during online class learning mode. There was no significant difference between the examinations in May 2020 and November 2020 (−3.20 ± 0.84 D, P = .21; Fig. 3), indicating no significant increase in myopia during school learning mode. For axial length in oculus dexter, we found that there was no difference between the baseline (24.55 ± 0.79 mm) and 6-month (24.89 ± 0.80 mm) examinations or between the 6- and 12-month (25.05 ± 0.89 mm) examinations. However, there was a significant difference between the baseline (November 2019) and 12-month (November 2020) examinations (P = .01).
FIGURE 3: (A) Changes in the average SER in the three data collection periods. (B) Change in the AL in the three periods. *P < .05; **P < .01. AL = axial length; OD = oculus dexter; OS = oculus sinister; SER = spherical equivalent refraction.
Correlation between Visual Behavior and Myopia Progression
As shown in Fig. 4, the glasses-wearing time and axial length growth had a negative correlation (R = −0.21, P < .001); outdoor time and axial length growth also had a negative correlation (R = −0.089, P < .05). In addition, near-viewing time and spherical equivalent refraction growth showed a positive correlation (R = 0.14, P < .01).
FIGURE 4: Glasses-wearing time and D-AL, as well as outdoor time and D-AL, had a negative correlation. Near-viewing time and D-SER had a positive correlation. *P < .05; **P < .01; ***P < .001. A = glasses-wearing time; B = outdoor time; C = near-viewing time; D-AL = axial length growth; D-AL(L) = left-eye axial length growth; D-AL(R) = right-eye axial length growth; D-SER = spherical equivalent refraction growth; D-SER(L) = spherical equivalent refraction growth of left eye; D-SER(R) = spherical equivalent refraction growth of right eye.
DISCUSSION
In our study, we included a total of 49 children with myopia and recorded their visual behavior using Akeso smart glasses during the periods of online learning mode and traditional school learning mode. Compared with traditional school mode, the online class mode increased near-viewing time and reduced outdoor time; this may lead myopia progressed more quickly while learning in online class mode. It should be noted that glasses-wearing time may also affect myopia progression.
Research on the relationship between visual behavior and myopia progression has been extensively studied and confirmed.8,10,11 Numerous studies have shown that outdoor time is a protective factor against myopia,8,23,24 near work is a risk factor for myopia,16,25,26 and use of digital screens increases near-viewing time.16,27,28 However, most past studies have involved questionnaires or cross-sectional designs.29,30 Among these studies, Li et al.31 found that increased extracurricular near-viewing time was significantly correlated with axial growth and equivalent spherical changes, and Bez et al.32 found that near-viewing time was associated with the prevalence and severity of myopia. In this study, we found a positive correlation between near-viewing time and an increase in equivalent spherical diopter. The longer the near-viewing time, the faster the increase in myopia. Acceleration of the progression of myopia in online class learning mode may be related to the increase in near-viewing time, which is consistent with the results of previous studies using questionnaires.
In previous studies, Cohen et al.33 found that, with lower light intensity, the axial length was longer and myopic refractive state higher, indicating that low ambient light intensity is a risk factor for myopia. He et al.9 found that increasing outdoor time can reduce the incidence of myopia, and Wu et al.34 showed that outdoor lighting can slow the incidence of myopia. In this study, smart glasses were used to objectively record visual behavior among participants, and we found a negative correlation between outdoor activity time and axial growth. Longer outdoor activity time was associated with slower axial growth. Huang et al.35 found that activation of the D2R receptor–mediated signal transduction pathway can slow the development of myopia, and a protective effect of light on myopia may be achieved by activating dopamine D2R receptors. Donovan et al.36 found that myopia progression in summer was less than in winter because there are seasonal variations in the myopia progression. It is unclear whether this phenomenon is a result of light intensity or outdoor time; hence, further studies about the role of different seasonal light intensity and outdoor time in myopia is needed to be explored. In our study, compared with the online class mode in winter, outdoor time was significantly increased with the traditional school mode in summer; this may be the reason why myopia progression in the online mode is faster than in the school mode.
Several studies on the visual behavior of children with and without myopia have confirmed the association between near work, outdoor activity, and myopia.5 During the COVID-19 pandemic, to decrease disease transmission, students were requested to stay at home and required to take online classes, leading to an extension of near-viewing time and reduction of outdoor activity,37,38 which are associated with myopic shift in younger school-aged children.39
Interestingly, in our study, there was no difference in visual behaviors between the online and school modes on the weekends. Our findings were in line with the results from an objective recording study by Williams et al.21: children may not study as much on weekend. We also sought to compare the average daily temporal patterns of outdoor time and near-viewing time between these two learning modes. Although the overall patterns were similar, the two modes showed some differences in specific episodes on weekdays. For example, online class mode required more near-viewing during study hours (Fig. 2). Between 4:00 pm and 5:00 pm in the school mode, children spent significantly more time outdoors, corresponding to the time after school (Fig. 1). Compared with the traditional school mode, online class mode involved more near-viewing time and less outdoor time on weekdays.
We found that glasses-wearing time was negatively correlated with the amount of axial length growth. In previous studies, Messer et al.40 found poor compliance among children with respect to glasses-wearing time. Maconachie et al.41 used a spectacle monitor to objectively measure glasses-wearing time and found that compliance with wearing glasses was highly variable. Further research is needed to explore the effect of glasses-wearing time on the progression of myopia.
Limitations in this study include the small sample size and lack of nonmyopic children. The strength of the study is that both outdoor activity time data and near-viewing time data were objectively recorded by wearable devices.
In conclusion, this study provides evidence of the association of learning mode and myopia progression. Accelerated progression of myopia in online class mode may be related to increased near-viewing time and decreased time spent in outdoor activities. This study identifies associations, but a larger sample in further research is required to verify the results.
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