Evidence map›Paper›PMID 30086711›Full record

Observational studyEnvironmental health and preventive medicine2018

Relationship between screen time and nutrient intake in Japanese children and adolescents: a cross-sectional observational study.

Hiromasa Tsujiguchi, Daisuke Hori, Yasuhiro Kambayashi, Toshio Hamagishi, Hiroki Asakura, Junko Mitoma, Masami Kitaoka, Enoch Olando Anyenda, Thao Thi Thu Nguyen, Yohei Yamada and 6 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Environmental health and preventive medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
14.9field-weighted citation impact, top 1% 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Observational
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Effect of Covid-19 lockdown on indian children with autism.Research in developmental disabilities · 2022
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

16 authors at 3 institutions in 1 country.

Hiromasa TsujiguchiDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan. t-hiromasa@med.kanazawa-u.ac.jp.ORCID http://orcid.org/0000-0001-7511-0165
Daisuke HoriDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Yasuhiro KambayashiDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Toshio HamagishiDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Hiroki AsakuraDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Junko MitomaDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Masami KitaokaDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Enoch Olando AnyendaDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Thao Thi Thu NguyenDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Yohei YamadaDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Koichiro HayashiDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Tadashi KonoshitaThird Department of Internal Medicine, University of Fukui Faculty of Medical Sciences, 23-3 Matsuokashimoaiduki, Eiheiji, Fukui, Japan.
Takiko SagaraDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Aki ShibataDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Satoshi SasakiDepartment of Social and Preventive Epidemiology, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyoku, Tokyo, Japan.
Hiroyuki NakamuraDepartment of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, Japan.
Kanazawa University · JPThe University of Tokyo · JPUniversity of Fukui · JP

Funding

Japan Society for the Promotion of Science 15K08729
6 · The paper itself

Abstract

backgroundSedentary behaviors have recently become an important public health issue. We aimed to investigate the relationship between screen time and nutrient intake in children and adolescents.

methodsThe present study was conducted in 2013. Data were collected from children and adolescents aged between 6 and 15 years old in Shika town. Questionnaires were distributed to 1459 subjects, 1414 of whom participated in the study (96.9%). Sedentary behaviors were assessed based on participants' screen behaviors (television (TV) viewing, personal computer (PC) use, and mobile phone (MP) use). The main outcomes were the intake of nutrients from a validated food frequency questionnaire. Analysis of covariance (ANCOVA) was used to examine the significance of differences in nutrient intake estimates. Multivariate linear regression analyses, adjusting for age, BMI, and physical activity, were used to provide parameter estimates (β) and 95% CI for the relationship between screen time and nutrient intake.

resultsIn boys, longer TV viewing times correlated or tended to correlate with a lower intake of protein, potassium, calcium, iron, vitamin K, vitamin B-2, and total dietary fiber. In girls, longer TV viewing times correlated with a lower intake of protein, sodium, calcium, vitamin D, and vitamin B-2. Longer TV viewing times correlated with a higher intake of n-6 fatty acids in girls. PC use was related or tended to be related to a lower intake of potassium, iron, vitamin K, and folic acid in boys, but not in girls. A relationship was observed between MP use and a lower intake of vitamin K in boys, and MP use and a higher intake of vitamin D in girls.

conclusionsThe present results revealed that longer TV viewing times are associated with less protein, minerals, vitamins, and total dietary fiber intake in children and adolescents. It was also revealed that boys with PC use have less minerals and vitamins. These results support the need to design intervention programs that focus on decreasing TV viewing time in both sexes and PC use in boys while encouraging adherence to dietary guidelines among children and adolescents.

Indexed as

DietScreen TimeAdolescentBody Mass IndexChildCross-Sectional StudiesEnergy IntakeExerciseFemaleHumansJapanMaleSedentary BehaviorSex FactorsSocioeconomic FactorsAdolescentsChildrenNutrientsScreenTelevision

Identifiers

PMID30086711
PMCPMC6081878
OpenAlexW2886235208

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.