This study investigated screen-time behaviors of Chinese children and adolescents and the efficacy of artificial intelligence (AI)-based alerts in screen-time behavior correction. Data from 6,716 children and adolescents with AI-enhanced tablets that recorded behavioral and light environment data during use were analyzed. The mean daily screen time was 67.10 ± 48.26 min. The screen time of junior-high-school students exceeded 1.5 h (92.50 ± 75.06 min) and that of school-aged participants exceeded 4 h per week. Children younger than two years used tablets for more than 45 min per day. Learning accounted for more than 50% of participants’ screen time. The distance alarm was triggered 807,355 times. Some participants (31.03%, 2061/6643) used tablets for 1 h at an average distance < 50 cm. Over 70% of the participants used the tablet under an illuminance < 300 lx during the day and more than 60% under an illuminance < 100 lx at night. More than 85% of the participants’ ambient light exceeded 4,000 K color temperature at night. The screen time of school-aged participants was longer than that of preschool-aged children. The recorded illumination was insufficient and was paired with a high color temperature at night. AI can effectively remind children and adolescents to correct unhealthy behaviors during screen time.
Article
Big data-based artificial intelligence for efficient digital screen usage management among Chinese children and adolescents during the COVID-19 pandemic
https://doi.org/10.21203/rs.3.rs-3297835/v1
This work is licensed under a CC BY 4.0 License
Version 1
posted
You are reading this latest preprint version
In recent decades, the global electronics industry has developed rapidly, accelerating the advent of the digital information age. Currently, various digital products, including desktop computers, laptops, digital televisions, and smartphones, affect nearly all aspects of people’s lives. For instance, traditional forms of work, learning, social communication, and entertainment are being replaced by telecommuting, remote courses, cyber communication, and online entertainment, respectively. The use of digital devices is efficient and convenient but prolongs near work and screen time, leading to video display terminal syndrome (or computer vision syndrome) [1–3].
During the COVID-19 pandemic, widespread school closures and in-house quarantines popularized remote education, telecommuting, and online entertainment, while the prevalence of myopia increased worldwide [4–6]. The World Health Organization released statistical data showing that the myopic population in China exceeded 700 million [7]. By 2050, the global prevalence of myopia is estimated to exceed 50% [8]. Various environmental factors, such as increased digital screen time, near work, and limited outdoor and dim-light exposure, are considered major risks of myopia onset and progression in children and adolescents, except for genetic factors [9, 10]. Moreover, using digital screens at a short distance, with poor posture, under poor illumination, or improper color temperature, increases the incidence of myopia [11, 12], strabismus [13], anisometropia [2], postural scoliosis [14], and abnormalities in musculoskeletal development among youth [15]. However, children and adolescents often use electronic devices in an unsupervised environment, and thus, little is known about their screen-time behaviors, such as daily usage schedules, duration, frequency, viewing content, viewing posture, and viewing distance, and the ambient light environments, such as illuminance and color temperature.
This study employed commercially available artificial intelligence (AI)-enhanced tablets combined with online questionnaires to map screen-time behaviors and luminous environments in Chinese children and adolescents during the COVID-19 pandemic. Furthermore, this study investigated the efficacy of AI-based visual and audible alerts in screen-time behavior correction among children and adolescents.
Participants and ethical approval
The study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants and their legal guardians, who agreed to disclose the identifying information in an online open-access publication. The study protocol was approved by the Ethics Committee of the Fudan University Eye and ENT Hospital Review Board (No. 2022113).
In the cross-sectional study, participants were users of commercially available AI-enhanced tablets (manufactured by BOE Yiyun Technology Co., Ltd., Changsha, Hunan Province, China; Model A127CS 6 + 128G) between October 9, 2022 and December 31, 2022. Participants aged over 18 years were excluded. Anonymous electronic survey questionnaires were sent to the participants and their legal guardians to verify the valid use of the tablets and collect information on the age and gender of the recruited children and adolescents. The tablets continuously recorded behavioral characteristics, including screen-use duration, frequency, content, viewing distance, body posture, head position, eye position, and blinking frequency, and parameters of luminous environments (illumination and color temperature) during their use from October 9, 2022 to December 31, 2022. Data from 6,716 children and adolescents (3,526 boys and 3,190 girls) were collected. After eliminating participants with missing data, data from 6,643 children and adolescents (3,485 boys and 3,158 girls, mean age 6.1 ± 3.0 years, 0–18 years old) were collected from 25 provinces and four cities across mainland China. Participants were divided into five groups according to age: participants aged from 0 to 2 years (before preschool) were enrolled in Group BPS, 3 to 5 years (preschool) in Group PS, 6 to 11 years (elementary school) in Group ES, 12 to 14 years (junior high school) in Group JHS, and 15 to 18 years (senior high school) in Group SHS.
Equipment
The tablets were 279 × 215.4 × 7.2 mm in size equipped with a capacitive touch screen (12.7 in) for display and interaction, main front digital camera (16 megapixels) for photography and AI action recognition, secondary front digital camera (5 megapixels) for posture monitoring, rear digital camera (13 megapixels), flash, range sensor (ranging from 1 to 250 cm), acceleration sensor, and ambient light sensor (Fig. 1a). For privacy protection, only features of the body, head, and eye outlines were extracted and analyzed.
Measurements and algorithm
Screen-time duration and period
After each user logged into their account, the tablet usage time was accumulated when the camera obtained a stable image of their facial outline. The average daily and weekly tablet usage times were calculated using formulas (1) and (2):
Sixty-eight applications (apps) with the most usage during the study period were classified into social networking apps, search engines, entertainment apps, and education apps. The duration of use (accumulated usage time) and time intervals (morning, 6:00–11:00; noon, 11:00–13:00; afternoon, 13:00–16:00; evening, 16:00–18:00; night, 18:00–24:00; and before dawn, 0:00–6:00) were recorded for each category.
Ambient illumination and color temperature
The ambient illumination and color temperature were measured during screen time using the ambient light sensor. The ambient illuminance values obtained during three time intervals (daytime, 8:00 to 20:00; nighttime, 20:00 to 8:00; and the entire day) were respectively divided into seven illumination intervals (rows): (0 lx,100 lx), [100 lx,300 lx), [300 lx, 500 lx), [500 lx, 1,000 lx), [1,000 lx, 2,000 lx), [2,000 lx,4,000 lx), and [4,000 lx,+∞ lx). In each time interval, the exposure time ratio of each illuminance interval was calculated as
The color temperature values obtained during the three time intervals were divided into four intervals: (0 K, 3,000 K), [3,000 K, 4,000 K), [4,000 K, 6,000 K), and [6,000 K, +∞ K). In each time interval, the exposure time ratio of each color temperature interval was calculated as
Viewing distance
The viewing distance was measured using a range sensor combined with the main front camera of the tablet. After the main camera captured the facial outline, the distance between the screen and face plane was obtained by the range sensor every 3 s. The viewing-distance data were considered valid only when the angle between the screen and desktop was 90° to 130° (Fig. 1b). The average viewing distance was calculated and analyzed. When the viewing distance was detected to be less than 30 cm for 12 s, an audio alert was played for 10 s to remind users to maintain a sufficient distance during screen time. If the user corrected the viewing distance to beyond 30 cm in 10 s, the alert was stopped and recorded as effective. If the user ignored the alert, a pop-up notification appeared on the screen, interrupting the user’s interaction with the tablet until the user adjusted the viewing distance to beyond 35 cm. The total number of audio alerts and pop-up notifications was recorded, and the effective alert ratio was calculated.
Viewing posture identification and alerts
Head posture refers to the position and orientation of the three-dimensional space of the head. Head postures are described by the egocentric rotation angles of pitch, roll, and yaw [16]. Using a low-resolution thermal map regression model, semi-supervised learning with a self-training algorithm was employed. To increase the robustness of the model, generative adversarial networks (GANs) were used to generate facial data for different ages, skin colors, and angles. A total of 300,000 images (100,000 labeled and 200,000 unlabeled) were included in the training set and 10,000 images were included in the testing set (Fig. 2).
In this study, the head was considered to be tilted when the roll angle exceeded ± 30° for more than 12 s. When a tilted head was detected, an audio alert was played to remind the user to correct their head posture within the next 10 s. If the alert was ignored by the user, a pop-up notification appeared on the screen to interrupt the tablet use until the user corrected their head pose. The total number of audio alerts and pop-up notifications were recorded, and the effective alert ratio was calculated.
Statistical analysis
Statistical analyses were performed using SPSS version 20.0. Data were presented as mean ± standard deviation. Mixed-effects models were used to analyze differences in usage time and viewing distance, with age brackets as the intra-group factors. Additionally, R×C chi-square tests were employed to compare the difference in the effective alert ratios of viewing distance and head posture among age brackets.
Demographics
Among the 6,643 Chinese children and adolescents recruited for this study, Groups BPS, PS, ES, JHS, and SHS included 607, 2,370, 3,370, 204, and 92 participants, respectively. The GDP values of the geographical areas where these participants were located and the mean age and gender of the participants from each area are listed in Table 1.
| Province/City | Sample Size | Gender | Age | Per Capita GDP in 2022 | Daily Tablet Usage Time | |||
|---|---|---|---|---|---|---|---|---|
| Boys:Girls | (years) | (RMB yuan) | (Minutes) | |||||
| Beijing | 1,392 | 719:673 | 6.2 ± 3.0 | 190,091 | 69 ± 49 | |||
| Shanghai | 367 | 189:178 | 5.6 ± 2.6 | 179,401 | 59 ± 43 | |||
| Yunnan | 61 | 36:25 | 6.2 ± 3.1 | 61,736 | 58 ± 47 | |||
| Inner Mongolia | 112 | 67:45 | 5.8 ± 2.8 | 96,496 | 58 ± 39 | |||
| Jilin | 150 | 91:59 | 6.1 ± 2.9 | 55,033 | 70 ± 50 | |||
| Sichuan | 246 | 122:124 | 5.8 ± 2.7 | 67,785 | 48 ± 37 | |||
| Tianjin | 265 | 148:117 | 6.2 ± 2.8 | 118,801 | 67 ± 51 | |||
| Ningxia | 15 | 8:7 | 6.2 ± 2.3 | 69,925 | 76 ± 59 | |||
| Anhui | 151 | 70:81 | 5.7 ± 3.0 | 73,687 | 45 ± 33 | |||
| Shandong | 359 | 196:163 | 5.9 ± 3.3 | 85,793 | 61 ± 47 | |||
| Shanxi | 171 | 83:88 | 6.3 ± 2.8 | 73,686 | 66 ± 49 | |||
| Guangdong | 429 | 213:216 | 6.2 ± 3.0 | 101,796 | 51 ± 41 | |||
| Guangxi | 33 | 21:12 | 6.7 ± 3.9 | 52,215 | 44 ± 32 | |||
| Xinjiang | 32 | 20:12 | 4.8 ± 2.3 | 68,526 | 52 ± 47 | |||
| Jiangsu | 405 | 224:181 | 5.6 ± 3.0 | 144,475 | 44 ± 35 | |||
| Jiangxi | 85 | 38:47 | 6.1 ± 2.5 | 71,009 | 45 ± 26 | |||
| Hebei | 252 | 135:117 | 6.4 ± 3.1 | 56,888 | 72 ± 51 | |||
| Henan | 199 | 106:93 | 6.2 ± 3.1 | 62,071 | 63 ± 44 | |||
| Zhejiang | 320 | 162:158 | 5.8 ± 3.0 | 118,830 | 40 ± 35 | |||
| Hainan | 63 | 38:25 | 6.0 ± 3.3 | 66,845 | 56 ± 39 | |||
| Hubei | 315 | 168:147 | 6.8 ± 3.1 | 92,170 | 83 ± 60 | |||
| Hunan | 94 | 47:47 | 5.8 ± 2.8 | 73,498 | 48 ± 35 | |||
| Gansu | 35 | 17:18 | 5.9 ± 2.5 | 44,986 | 65 ± 47 | |||
| Fujian | 146 | 79:76 | 5.7 ± 2.8 | 126,845 | 45 ± 34 | |||
| Guizhou | 30 | 17:13 | 5.8 ± 2.5 | 52,348 | 28 ± 15 | |||
| Liaoning | 404 | 218:186 | 6.2 ± 2.9 | 68,515 | 65 ± 49 | |||
| Chongqing | 140 | 74:66 | 5.5 ± 3.0 | 90,688 | 67 ± 50 | |||
| Shaanxi | 175 | 85:90 | 6.1 ± 3.1 | 82,885 | 48 ± 31 | |||
| Heilongjiang | 197 | 103:94 | 6.0 ± 2.8 | 50,883 | 71 ± 52 | |||
| SD = standard deviation; GDP = gross domestic product | ||||||||
Usage time
The mean daily usage times of all participants (n = 6,643), boys (n = 3,485), and girls (3,158) were 67.10 ± 48.26 min, 65.82 ± 45.84 min, and 68.52 ± 50.75 min, respectively. The daily usage time of girls was significantly longer than that of boys (t = -2.2591, P = 0.0239). The mean daily usage times and sample sizes of each group are listed in Table 2. A significant difference in daily usage time was observed among the five groups (F = 255.72, P < 0.001). Bonferroni post hoc tests showed that the daily usage time of Group JHS was significantly longer than that of the other groups (all post hoc P < 0.05); the daily usage times of Groups ES and SHS were the second longest (post hoc P = 0.354) and longer than those of Groups BPS and PS (all post hoc P < 0.001). The daily usage times of Groups BPS and PS were similar (post hoc P = 1.000) (Fig. 3a).
| Age Groups | Sample Size | Daily Usage Time (Minutes) | Fa | Pb | Post hoc Pc |
|---|---|---|---|---|---|
| BPS (0–2 yrs) | 607 | 48.08 ± 38.18 | BPS vs PS, P = 1.000 | ||
| BPS vs ES, P < 0.001* | |||||
| PS (3–5 yrs) | 2,370 | 47.57 ± 30.61 | BPS vs JHS, P < 0.001* | ||
| BPS vs SHS, P < 0.001* | |||||
| ES (6–11 yrs) | 3,370 | 82.58 ± 51.14 | 225.72 | < 0.001 | PS vs ES, P < 0.001* |
| PS vs JHS, P < 0.001* | |||||
| JHS (12–14 yrs) | 204 | 92.50 ± 75.06 | PS vs SHS, P < 0.001* | ||
| ES vs JHS, P = 0.022* | |||||
| SHS (15–18 yrs) | 92 | 72.59 ± 61.70 | ES vs SHS, P = 0.354 | ||
| JHS vs SHS, P = 0.004* | |||||
| BPS = before preschool; PS = preschool; ES = elementary school; JHS = junior high school; SHS = senior high school; yrs = years; a,b = Mixed-effects model; c = Bonferroni post hoc tests; * = statistically significant | |||||
The weekly usage time of all the groups is listed in Table 3. The weekly usage times of Groups ES, JHS, and SHS were significantly longer than those of Groups BPS (all post hoc P ≤ 0.001) and PS (all post hoc P < 0.001). However, no significant differences were detected in the weekly usage time between Groups BPS and PS (post hoc P = 1.000) and Groups ES, JHS, and SHS (all post hoc P > 0.05) (Fig. 3b).
| Age Groups | Sample Size | Weekly Usage Time (Hours) | Fa | Pb | Post hoc Pc |
|---|---|---|---|---|---|
| BPS (0–2 yrs) | 607 | 192.81 ± 187.02 | BPS vs PS, P = 1.000 | ||
| BPS vs ES, P < 0.001* | |||||
| PS (3–5 yrs) | 2,370 | 190.83 ± 159.64 | BPS vs JHS, P < 0.001* | ||
| BPS vs SHS, P = 0.001* | |||||
| ES (6–11 yrs) | 3,370 | 354.17 ± 268.45 | 198.96 | < 0.001 | PS vs ES, P < 0.001* |
| PS vs JHS, P < 0.001* | |||||
| JHS (12–14 yrs) | 204 | 360.84 ± 379.82 | PS vs SHS, P < 0.001* | ||
| ES vs JHS, P = 1.000 | |||||
| SHS (15–18 yrs) | 92 | 296.76 ± 295.78 | ES vs SHS, P = 0.200 | ||
| JHS vs SHS, P = 0.288 | |||||
| BPS = before preschool; PS = preschool; ES = elementary school; JHS = junior high school; SHS = senior high school; yrs = years; a, b = Mixed-effects model; c = Bonferroni post hoc tests; * = statistically significant | |||||
Luminance and ambient lighting color temperature
The luminance detected by the ambient light sensor of the tablet computer during the different periods is listed in Table 4. Table 5 shows the distribution of ambient lighting color temperature during screen time.
| Luminance\Time | Entire Day | Daytime (8:00–20:00) | Night-time (20:00–8:00) |
|---|---|---|---|
| 0–100 lx | 39.62% (95,113/240,064) | 36.16% (73,405/203,001) | 61.11% (22,649/37,063) |
| 100–300 lx | 33.42% (80,229/240,064) | 34.49% (70,015/203,001) | 25.02% ( 9,273/37,063) |
| 300–1,000 lx | 19.96% (47,916/240,064) | 21.78% (44,213/203,001) | 9.74% ( 3,609/37,063) |
| 1,000–4,000 lx | 7.00% (16,804/240,064) | 7.57% (15,367/203,001) | 4.13% ( 1,530/37,063) |
| 1 lx = 1 lumen per square meter | |||
| Illumination\Time | Entire Day | Daytime (8:00–20:00) | Night-time (20:00–8:00) |
|---|---|---|---|
| < 4,000 K | 9.17% ( 20,123/219,447) | 8.22% (15,413/187,515) | 14.91% ( 4,761/31,932) |
| 4,000–6,000 K | 61.65% (135,290/219,447) | 63.89% (119,803/187,515) | 49.14% (15,691/31,932) |
| 6,000–8,000 K | 25.86% ( 56,749/219,447) | 24.87% (46,635/187,515) | 31.01% ( 9,902/31,932) |
| > 8,000 K | 3.32% ( 7,285/219,447) | 3.02% (5,662/187,515) | 4.94% ( 1,577/31,932) |
| K = Kelvin |
Viewing content
The top 48 most commonly used apps were filtered from 5,424 apps and classified into four categories: social networking, search engines, entertainment, and education. Their session durations were recorded (Table 6). Educational applications accounted for 54.88% (3,646/6,643) of the sessions over the entire day, including the largest proportion of all session durations except for in the before dawn period; entertainment applications ranked first (48.33%; 478/988) in this period.
| Time/Apps | Sample Size | Accumulated Duration (Hours) | Per Capita Duration (Hours) | Social Networking(%) | Search Engine (%) | Entertainment(%) | Education (%) | Total (%) |
|---|---|---|---|---|---|---|---|---|
| 00:00–06:00 (Before Dawn) | 988 | 1,003.70 | 0.24 ± 0.47 | 10.58 | 17.22 | 48.33 | 23.87 | 100 |
| 06:00–11:00 (Morning) | 5,985 | 54,339.83 | 0.38 ± 0.32 | 11.92 | 7.32 | 23.05 | 57.71 | 100 |
| 11:00–13:00 (Noon) | 5,957 | 21,280.67 | 0.28 ± 0.24 | 10.71 | 7.14 | 25.01 | 57.14 | 100 |
| 13:00–16:00 (Afternoon) | 6,000 | 39,772.64 | 0.31 ± 0.24 | 13.06 | 17.29 | 22.85 | 46.80 | 100 |
| 16:00–18:00 (Evening) | 5,948 | 20,763.50 | 0.28 ± 0.23 | 12.29 | 7.02 | 28.09 | 52.60 | 100 |
| 18:00–24:00 (Night) | 6,253 | 43,760.53 | 0.26 ± 0.21 | 5.28 | 8.67 | 26.63 | 59.42 | 100 |
| 00:00–24:00 (Entire Day) | 6,643 | 180,920.85 | 2.46 ± 1.84 | 10.46 | 9.84 | 24.82 | 54.88 | 100 |
Efficacy of AI models in viewing-distance and posture alert
Tables 7 and 8 show that 6,378 and 6,403 participants used viewing-distance and posture monitoring, respectively. Of the subjects, 83.76% (5,564/6,643) used the tablets with an average viewing distance of less than 50 cm, 60.71% (4,033/6,643) used the tablets continuously for 1 h, and 31.03% (2,061/6,643) used the tablets continuously for 1 h with a viewing distance of less than 50 cm. In total, 807,355 and 509,199 improper viewing-distance and posture alerts were triggered, respectively. The ES group had the highest per-capita alerts (479.58) of viewing distance among all groups. The efficacy of viewing-distance correction among the groups was significantly different (χ2 = 1,174.165, P < 0.001). The efficacy of viewing-distance correction in the JHS group (324,269/479,567; 69.67%) was the highest of all groups (all post hoc P < 0.05), whereas that in Group BPS (25,442/41,409; 61.44%) was the lowest (all post hoc P < 0.05). The per-capita posture alerts were the highest in Group BPS (90.58). The efficacy of the AI alerts in posture correction differed among the groups (χ2 = 2,519.789, P < 0.001). The efficacies of the posture correction in Groups ES (235,254/267,951; 87.80%) and SHS (5,238/5,984, 87.62%) were significantly higher than those in the other groups (all post hoc P < 0.05). The difference in efficacy between Groups ES and SHS was nonsignificant (post hoc P > 0.05).
| Age Groups | Sample Size | Total Alert (times) | Per Capita Alert (times) | Valid Alert (times) | Invalid Alert (times) | Efficacy | χ2 | p |
|---|---|---|---|---|---|---|---|---|
| BPS | 562 | 41,409 | 73.68 | 25,442 | 15,967 | 61.44% a | 1174.165 | < 0.001 |
| PS | 2,283 | 254,870 | 111.64 | 164,947 | 89,923 | 64.72% b | ||
| ES | 3,259 | 479,567 | 479.58 | 324,269 | 155,298 | 67.62% c | ||
| JHS | 188 | 15,604 | 83.00 | 10,871 | 4733 | 69.67% d | ||
| SHS | 86 | 15,905 | 184.94 | 10,485 | 5420 | 65.92% e | ||
| Total | 6,378 | 807,355 | 126.58 | 536,014 | 271,341 | 66.39% | ||
| BPS = (0–2 yrs), PS = (3–5 yrs), ES = (6–11 yrs), JHS = (12–14 yrs), SHS = (15–18 yrs); yrs = years; a,b,c,d,e = Bonferroni post hoc tests (Pairwise comparisons between age groups were statistically significant, all post hoc P < 0.05) | ||||||||
| Age Groups | Sample Size | Total Alert (times) | Per Capita Alert (times) | Valid Alert (times) | Invalid Alert (times) | Efficacy | χ2 | p |
|---|---|---|---|---|---|---|---|---|
| BPS | 587 | 53,172 | 90.58 | 42,784 | 10,388 | 80.46% a | 2519.789 | < 0.001 |
| PS | 2,314 | 169,150 | 73.09 | 142,326 | 26,824 | 84.14% b | ||
| ES | 3,223 | 267,951 | 83.14 | 235,254 | 32,697 | 87.80% c | ||
| JHS | 195 | 12,942 | 66.37 | 10,952 | 1,990 | 84.62% b | ||
| SHS | 84 | 5,984 | 71.24 | 5,238 | 746 | 87.53% c | ||
| Total | 6,403 | 509,199 | 79.53 | 436,554 | 72,645 | 85.73% | ||
| BPS = (0–2 yrs), PS = (3–5 yrs), ES = (6–11 yrs), JHS = (12–14 yrs), SHS = (15–18 yrs); yrs = years; a = Bonferroni post hoc tests (Pairwise comparisons between the BPS Group and other groups were statistically significant, P < 0.05), b = Bonferroni post hoc tests (The difference between group PS and JHS was nonsignificant, P > 0.05), c = Bonferroni post hoc tests (The difference between group ES and SHS was nonsignificant, P > 0.05) | ||||||||
During the COVID-19 pandemic, most educational institutions substituted online courses for in-person classes. Students studied at home using electronic devices. As most outdoor activities were restricted, leisure time was filled with online entertainment, and the duration of near work dramatically increased. This study found that 0–2-year-old children used tablets for more than 45 min per day on average, violating the recommendation of the American Academy of Pediatrics to avoid digital media use (except video chatting) in children younger than 18 to 24 months [17].
Moreover, excessive digital screen use in early childhood may delay the development of cognition, language, and emotions [2]. The average screen time for primary-, middle-, and high-school students was more than 4 h (354.17 ± 268.45, 360.84 ± 379.82, 296.76 ± 295.78 min, respectively) per week, and that for middle-school students exceeded 1.5 h (92.50 ± 75.06 min) per day. Saxena et al. [18] reported that children and adolescents who use electronic screens for more than 4 h per week are at more than eight times higher risk of contracting myopia than those who do not use electronic screens. A systematic review and meta-analysis indicated that the prolonged use of electronic screens could significantly increase the axial eye length and risk of myopia [19]. Furthermore, excessive electronic screen time may replace outdoor activity time, reduce natural light exposure, increase the risk of obesity [20], and decrease the quality of sleep [21], further increasing the risk of myopia occurrence and progression.
Regarding the rhythm and distance of the tablets use, our data showed that the vast majority of participants (5,564/6,643, 83.76% ) had an average viewing distance of less than 50 cm, and some subjects (2,061/6,643, 31.03%) used the tablets continuously for 1 h at such viewing distances. The distance alarm was triggered 807,355 times. A study using apps to record the smartphone usage time of teenagers showed that in the group with fewer outdoor activities, the myopic diopter was lower for those who rested every 20 min after watching the screen [22]. Additionally, the continuous prolonged use of electronic display devices at close distance can cause asthenopia. The incidence of dry eye syndrome in children has reached 50–60%, and its symptoms include dry eyes, foreign body sensation, tears, blurred vision, headache, esotropia, and abnormal vision. A study found that the degree of asthenopia was the least when the screen was approximately 50 cm away from the eyes [3]. Reid et al. [23] proposed that children and adolescents should follow the 20-20-20 principle while using digital products: rest and look at an object 20 ft away for 20 s after using the devices for 20 min. According to a recent study, it is recommended to rest and look further for more than 5 min to relieve asthenopia [24].
This study found that except for the before dawn time, more than 50% of the time was used for learning. In the daytime, more than 70% of children and adolescents used tablets with illuminance less than 300 lx; at night, more than 85% of them used tablets with illuminance less than 300 lx, and more than 60% of them used tablets with illuminance less than 100 lx. Additionally, traditional work using paper media was often involved. These findings are in line with those of Ma et al. [25, 26] and indicate insufficient home illumination, which is consistent with the results of cloud clip [27] monitoring of daily white light exposure (253 ± 36 lx). According to the dark-focus theory, the refractive state of the human eye will drift toward myopia in a dim-light environment, which necessitates engaging in proximate tasks such as reading and writing [28, 29]. Furthermore, low illumination can easily cause a decrease in contrast on paper media, which reduces accommodation accuracy, causes blurred vision, aggravates asthenopia, and even induces myopia [30]. In addition, an electronic screen is a self-luminous medium, and a low-ambient light and luminous display screen will lead to uneven space illumination and glare.
When monitoring the light environment with electronic screens, we found that for more than 85% of the participants, the ambient light exceeded 4,000 K color temperature at night, and it exceeded 6,000 K color temperature for more than 35% of the participants. Some studies [31, 32] have suggested that high color temperature lighting at night disrupts the melatonin secretion cycle, influences sleep in children and adolescents, and adversely affects physical and psychological development. Sleep deprivation and poor sleep quality are risk factors for myopia occurrence and progression. In addition, the content displayed by the tablet computer inevitably contains high color temperature information, which can affect the hormone secretion rhythm in a specific period. We found that 1,612 participants used the tablets in the early morning (midnight to 6:00), whereas during lunch (11:00–13:00) and dinner (16:00–18:00), more than 6,000 of the total 6,643 participants were still recorded using electronic screens; however, the cumulative time was significantly lower than that in the morning, afternoon, and evening. The American Children’s Association recommends that children and adolescents avoid using electronic devices before and after meals and 1 h before going to sleep [17]. The vast majority (more than 90%) of the participants in this study violated this recommendation. The content on electronic screens in the early morning was primarily focused on entertainment. This period is likely to be without parental supervision. Therefore, it is necessary to assist parents in managing the light environment, time period, and content on children’s and adolescents’ electronic screens.
This study had some limitations. First, data were collected during the COVID-19 pandemic. The time, duration, and rhythm of children’s and adolescents’ screen time differed from those during the normal period. Second, the sample sizes extracted from different provinces were considerably different, and the deviation between the sampling results and the population may be large. Third, this study only monitored and investigated the screen-time behavior of children and adolescents during the use of BOE devices and did not monitor the participants’ use of other electronic devices, such as mobile phones, TVs, desktop computers, and tablets of other brands.
In summary, children of younger ages have started using electronic screens. Screen time for school-age children and adolescents with a closer viewing distance and higher incidence of poor viewing posture was longer than that for preschool children. During screen time, illumination was insufficient, paired with a high color temperature at night. AI can effectively monitor the time, content, and light environment during screen time and remind children and adolescents to correct their poor posture and behavior.
Acknowledgments: National Natural Science Foundation of China (Grant No. 82101183); Research Project Grant of Shanghai Municipal Commission of Health and Family Planning (20204Y0058); Shanghai Engineering Research Center of Laser and Autostereoscopic 3D for Vision Care (Grant No. 20DZ2255000); Construction of a 3D digital intelligent prevention and control platform for the whole life cycle of highly myopic patients in the Yangtze River Delta (Grant No. 21002411600);
Project of Shanghai Science and Technology (Grant No.20410710100); Clinical Research Plan of SHDC (Grant No. SHDC2020CR1043B); Project of Shanghai Xuhui District Science and Technology (Grant No. 2020-015, XHLHGG202104); Project of Shanghai Science and Technology (Grant No. 19140900700); Research Project of Shanghai Municipal Health Commission(Grant No.202340243); Nursing Research Fund of Fudan University (Grant No. FNF202234);Key Discipline Program of Sixth Round of the Three-year Public Health Action Plan (Year 2023-2025) of Shanghai, China: GWVI-11.1-32; National Natural Science Foundation of China (Grant No.82171095)
Author contributions: Study concept and design (YS, XTZ, YX, XYW); data collection (JFW, YCW, BW, YLW); data analysis and interpretation (YTH, TH, YYH); drafting of the manuscript (YS, JFW, JFH, JZ); critical revision of the manuscript (XTZ, YX, XYW, ZC, WXZ); supervision (XTZ, YX). All authors read and approved the final manuscript.
Data availability statement: The datasets generated and/or analyzed during the current study are available from the corresponding authors on reasonable request.
Informed consent statement: Informed consent was obtained from all participants and their legal guardians in the present study.
Additional Information
Competing Interests: Disclosure: Yuchen Wu, Bo Wen and Yongting Hong are employed by BOE Yiyun Technology CO., Ltd., who provided the tablets for this research. No other authors reported conflict of interest. The funding organization had no role in the design or conduct of this study.
- Alabdulkader, B. Effect of digital device use during COVID-19 on digital eye strain. Clin. Exp. Optom. 104, 698–704 (2021).
- Kaur, K. et al. Digital eye strain- A comprehensive review. Ophthalmol. Ther. 11, 1655–1680 (2022).
- Feng, D., Lu, C., Cai, Q. & Lu J. A study on the design of vision protection products based on children's visual fatigue under online learning scenarios. Healthcare 10, 621; 10.3390/healthcare10040621 (2022).
- Wang, G., Zhang, Y., Zhao, J., Zhang, J. & Jian, F. Mitigate the effects of home confinement on children during the COVID-19 outbreak. Lancet 395, 945–947 (2020).
- Chang, P. J. et al. Comparison of myopic progression before, during, and after COVID-19 lockdown. Ophthalmology 128, 1655–1657 (2021).
- Wang, J. et al. Progression of myopia in school-aged children after COVID-19 home confinement. JAMA Ophthalmol.139, 293–300 (2021).
- Jan, C. et al. Prevention of myopia, China. Bull. World Health Organ. 98, 435–437 (2020).
- Holden, B. A. et al. Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050. Ophthalmology 123, 1036–1042 (2016).
- Morgan, I. G. et al. IMI risk factors for myopia. Invest. Ophthalmol. Vis. Sci. 62, 3; 10.1167/iovs.62.5.3 (2021).
- Ku, P. W. et al. The associations between near visual activity and incident myopia in children- A nationwide 4-year follow-up study. Ophthalmology 126, 214–220 (2019).
- Yang, G. Y. et al. Associations between screen exposure in early life and myopia amongst Chinese preschoolers. Int. J. Environ. Res. Public Health 17, 1056; 10.3390/ijerph17031056 (2020).
- Hinterlong, J. E., Holton, V. L, Chiang, C. C., Tsai, C. Y. & Liu Y. M. Association of multimedia teaching with myopia: A national study of school children. J. Adv. Nurs.75, 3643–3653 (2019).
- Németh, J. et al. Update and guidance on management of myopia. European Society of Ophthalmology in cooperation with International Myopia Institute. Eur. J. Ophthalmol. 31, 853–883 (2021).
- Nault, M. L. et al. Relations between standing stability and body posture parameters in adolescent idiopathic scoliosis. Spine (Phila Pa 1976) 27, 1911–1917 (2002).
- David, D., Giannini, C., Chiarelli, F. & Mohn, A. Text neck syndrome in children and adolescents. Int. J. Environ. Res. Public Health 18, 1565; 10.3390/ijerph18041565 (2021).
- Murphy-Chutorian, E. & Trivedi, M. M. Head pose estimation in computer vision: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 31, 607–626 (2009).
- Council on Communications and Media et al. Media and young minds. Pediatrics 138, e20162591; 10.1542/peds.2016–2591 (2016).
- Saxena, R. et al. Prevalence of myopia and its risk factors in urban school children in Delhi: The North India Myopia Study (NIM Study). PLoS One 10, e117349; 10.1371/journal.pone.0117349 (2015).
- Foreman, J. et al. Association between digital smart device use and myopia: a systematic review and meta-analysis. Lancet Digit. Health 3, e806-e818 (2021).
- Mireku, M. O. et al. Night-time screen-based media device use and adolescents' sleep and health-related quality of life. Environ. Int. 124, 66–78 (2019).
- Robinson, T. N. et al. Screen media exposure and obesity in children and adolescents. Pediatrics 140, S97-S101 (2017).
- Enthoven, C. A. et al. Smartphone use associated with refractive error in teenagers: The Myopia App Study. Ophthalmology 128, 1681–1688 (2021).
- Reid Chassiakos, Y. L. et al. Children and adolescents and digital media. Pediatrics 138, e20162593; 10.1542/peds.2016–2593 (2016).
- Pucker, A. D. & Gawne, T. J. Fighting myopia with intermittent nearwork breaks: 20 seconds every 20 minutes might not be enough time. Optom. Vis. Sci. 100, 31–32 (2023).
- Ma, M. et al. COVID-19 home quarantine accelerated the progression of myopia in children aged 7 to 12 years in China. Invest. Ophthalmol. Vis. Sci. 62, 37; 10.1167/iovs.62.10.37 (2021).
- Kohmarn, T., Srisurattanamethakul, N., Watcharapalakorn, A., Poyomtip, T., Poolsanam, C. Outbreak of COVID-19-related myopia progression in adults: A preliminary study. Clin. Optom. (Auckl).14, 125–131 (2022).
- Bhandari, K.R., Shukla, D., Mirhajianmoghadam, H. & Ostrin, L. A. Objective measures of near viewing and light exposure in schoolchildren during COVID-19. Optom. Vis. Sci. 99, 241–252 (2022).
- Leibowitz, H. W. & Owens, D. A. Night myopia and the intermediate dark focus of accommodation. J. Opt. Soc. Am. 65, 1121–1128 (1975).
- Ebenholtz, S. M. & Zander, P. A. Accommodative hysteresis: influence on closed loop measures of far point and near point. Invest. Ophthalmol. Vis. Sci. 28, 1246–1249 (1987).
- Muralidharan, A. R. et al. Light and myopia: from epidemiological studies to neurobiological mechanisms. Ther. Adv. Ophthalmol. 13, 25158414211059246; 10.1177/25158414211059246 (2021).
- Osterhaus, W., Hemphälä, H. & Nylén, P. Lighting at computer workstations. Work 52, 315–328 (2015).
- Heo, J. Y. et al. Effects of smartphone use with and without blue light at night in healthy adults: A randomized, double-blind, cross-over, placebo-controlled comparison. J. Psychiatr. Res. 87, 61–70 (2017).
Competing interest reported. Yuchen Wu, Bo Wen and Yongting Hong are employed by BOE Yiyun Technology CO., Ltd, who provided the tablets for this research. No other authors reported conflict of interest. The funding organization had no role in the design or conduct of this study.