Evidence map›Paper›PMID 38454995›Full record

ArticleFrontiers in public health2024

Algorithmic and sensor-based research on Chinese children's and adolescents' screen use behavior and light environment.

Jifang Wang, Yang Shen, Jing Zhao, Xiaoying Wang, Zhi Chen, Tian Han, Yangyi Huang, Yuliang Wang, Wuxiao Zhao, Wen Wen and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Jifang Wang *Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Yang Shen *Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Jing Zhao *Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Xiaoying WangEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Zhi ChenEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Tian HanEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Yangyi HuangEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Yuliang WangEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Wuxiao ZhaoEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Wen WenEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Xingtao ZhouEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Ye XuEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Myopia poses a global health concern and is influenced by both genetic and environmental factors. The incidence of myopia tends to increase during infectious outbreaks, such as the COVID-19 pandemic. This study examined the screen-time behaviors among Chinese children and adolescents and investigated the efficacy of artificial intelligence (AI)-based alerts in modifying screen-time practices. Methods: A cross-sectional analysis was performed using data from 6,716 children and adolescents with AI-enhanced tablets that monitored and recorded their behavior and environmental light during screen time. Results: The median daily screen time of all participants was 58.82 min. Among all age groups, elementary-school students had the longest median daily screen time, which was 87.25 min and exceeded 4 h per week. Children younger than 2 years engaged with tablets for a median of 41.84 min per day. Learning accounted for 54.88% of participants' screen time, and 51.03% (3,390/6,643) of the participants used tablets for 1 h at an average distance <50 cm. The distance and posture alarms were triggered 807,355 and 509,199 times, respectively. In the study, 70.65% of the participants used the tablet under an illuminance of <300 lux during the day and 61.11% under an illuminance of <100 lux at night. The ambient light of 85.19% of the participants exceeded 4,000 K color temperature during night. Most incorrect viewing habits (65.49% in viewing distance; 86.48% in viewing posture) were rectified swiftly following AI notifications (all Conclusion: Young children are increasingly using digital screens, with school-age children and adolescents showing longer screen time than preschoolers. The study highlighted inadequate lighting conditions during screen use. AI alerts proved effective in prompting users to correct their screen-related behavior promptly.

Indexed as

Artificial IntelligenceMyopiaAdolescentChildChild, PreschoolChinaCross-Sectional StudiesHumansInfantPandemicsartificial intelligencedisplay terminallight environmentmyopiascreen timeviewing distance

Identifiers

PMID38454995
PMCPMC10917963
OpenAlexW4392079368

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.