CoChat
Sign Up
Cited by 70Impact of Technology on AdolescentsSleep and related disordersChild Development and Digital Technology

Problematic Internet use and its associations with health‐related symptoms and lifestyle habits among rural Japanese adolescents

Open access
Kojima, Reiji, Sato, Miri, Akiyama, Yuka, Shinohara, Ryoji, Mizorogi, Sonoko, Suzuki, Kohta, Yokomichi, Hiroshi, Yamagata, Zentaro·2019·Psychiatry and Clinical Neurosciences
DOI 10.1111/pcn.12791
Open this paper in CoChat — free accountOpen PDF

Abstract

Aim: There have been concerns about the increase in problematic Internet use (PIU) and its impact on lifestyle habits and health‐related symptoms, given the rapid spread of smartphones. This study aimed to clarify PIU prevalence over 3 years in the same area and investigate lifestyle and health‐related factors related to PIU among junior high‐school students in Japan. Methods: Each year during 2014–2016, a survey was conducted with junior high‐school students from a rural area of Japan (2014, n = 979; 2015, n = 968; 2016, n = 940). Young's Internet Addiction Test was used to assess participants’ PIU. Students scoring 40 or higher on the Internet Addiction Test were classified as showing PIU in this study. The associations between PIU and lifestyle factors (e.g., exercise habits, weekday study time, and sleep time) and health‐related symptoms (depressive symptoms and orthostatic dysregulation [OD] symptoms) were studied by logistic regression analyses. Results: Over the 3 years, the prevalence of PIU was 19.9% in 2014, 15.9% in 2015, and 17.7% in 2016 without significant change. PIU was significantly associated with skipping breakfast, having a late bedtime (after midnight), and having OD symptoms among students from all grades. Sleepiness after awakening in the morning, less studying time, and depressive symptoms had significant positive associations with PIU, except among 1st grade junior high‐school students. Conclusion: Our results suggest that PIU is related to decreased time spent sleeping, studying, and exercising and increased symptoms of depression and OD. Further investigations are needed to develop preventive measures for PIU.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/pcn.12791

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Adolescents addicted to the internet, including those addicted to social media, engaged in less exercise and physical activity and had less sleep.

    “Kojima et al. (2019) found that in general, adolescents addicted to the internet (including those addicted to social media) engaged in less exercise and physical activity and had less sleep.”

    From Ethics of the Attention Economy: The Problem of Social Media Addiction (2021) · cites this paper as Kojima et al. (2019)

    The Discussion (page unavailable) suggests that problematic Internet use among rural Japanese junior-high students is associated with reduced sleep and exercise time. The Results (page unavailable) qualify the exercise finding: lack of exercise was significantly associated with problematic Internet use only among first-grade students in the adjusted analysis. The paper asks about social-networking use but does not establish the claimed sleep and exercise pattern specifically for adolescents addicted to social media; its findings also do not establish the pattern for adolescents in general. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 32

  1. Spada MM. An overview of problematic internet use. Addict. Behav. 2014; 39: 3-6.
  2. Choi K, Son H, Park M et al. Internet overuse and excessive daytime sleepiness in adolescents. Psychiatry Clin. Neurosci. 2009; 63: 455-462.
  3. Kamal NN, Mosallem FA. Determinants of problematic internet use among El-Minia high school students, Egypt. Int. J. Prev. Med. 2013; 4: 1429-1437.
  4. Lam LT. Risk factors of internet addiction and the health effect of inter- net addiction on adolescents: A systematic review of longitudinal and prospective studies. Curr. Psychiatry Rep. 2014; 16: 508.
  5. Fu KW, Chan WS, Wong PW, Yip PS. Internet addiction: Prevalence, discriminant validity and correlates among adolescents in Hong Kong. Br. J. Psychiatry 2010; 196: 486-492.
  6. Kim K, Ryu E, Chon MY et al. Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: A questionnaire sur- vey. Int. J. Nurs. Stud. 2006; 43: 185-192.
  7. Lam LT, Peng ZW. Effect of pathological use of the internet on adoles- cent mental health: A prospective study. Arch. Pediatr. Adolesc. Med. 2010; 164: 901-906.
  8. Tang J, Zhang Y, Li Y et al. Clinical characteristics and diagnostic con- firmation of internet addiction in secondary school students in Wuhan, China. Psychiatry Clin. Neurosci. 2014; 68: 471-478.
  9. Okada T, Murata M, Suzuki J et al. Recommendations on the issues of information and communication technology among children. J. Child Health 2015; 74: 1-4 (in Japanese).
  10. Mihara S, Osaki Y, Nakayama H et al. Internet use and problematic internet use among adolescents in Japan: A nationwide representative survey. Addict. Behav. Rep. 2016; 4: 58-64.
  11. Kawabe K, Horiuchi F, Ochi M, Oka Y, Ueno S. Internet addiction: Prevalence and relation with mental states in adolescents. Psychiatry Clin. Neurosci. 2016; 70: 405-412.
  12. Tanaka H, Fujita Y, Takenaka Y et al. Japanese clinical guidelines for juvenile orthostatic dysregulation version 1. Pediatr. Int. 2009; 51: 169-179.
  13. Tsuchiya A, Kitajima T, Tomita S, Esaki Y, Hirose M, Iwata N. High prevalence of orthostatic dysregulation among circadian rhythm disorder patients. J. Clin. Sleep Med. 2016; 12: 1471-1476.
  14. Young KS. Caught in the Net: How to Recognize the Signs of Internet Addiction and a Winning Strategy for Recovery. John Wiley & Sons, New York, 1998.
  15. Birleson P. The validity of depressive disorder in childhood and the development of a self-rating scale: A research report. J. Child Psychol. Psychiatry 1981; 22: 73-88.
  16. Murata T, Shimizu A, Mori Y, Ousima S. Childhood depressive state in the school situation -consideration from the Birleson's Scale. Jpn. J. Psychiatry 1996; 1: 131-138 (in Japanese).
  17. Guertler D, Rumpf HJ, Bischof A et al. Assessment of problematic inter- net use by the Compulsive Internet Use Scale and the Internet Addiction Test: A sample of problematic and pathological gamblers. Eur. Addict. Res. 2014; 20: 75-81.
  18. Lee K, Lee HK, Gyeong H, Yu B, Song YM, Kim D. Reliability and validity of the Korean version of the Internet Addiction Test among col- lege students. J. Korean Med. Sci. 2013; 28: 763-768.
  19. Korkeila J, Kaarlas S, Jaaskelainen M, Vahlberg T, Taiminen T. Attached to the web -Harmful use of the internet and its correlates. Eur. Psychiatry 2010; 25: 236-241.
  20. Denda K, Kako Y, Kitagawa N, Koyama T. Assessment of depressive symptoms in Japanese school children and adolescents using the Birleson Depression Self-Rating Scale. Int. J. Psychiatry Med. 2006; 36: 231-241.
  21. Kazuma N. Evaluation of a screening checklist and new diagnostic cri- teria for juvenile orthostatic dysregulation. J. Jpn. Pediatr. Soc. 2014; 118: 784-790.
  22. Mihara S, Kiyayuguchi T, Higuchi S. A treatment camp and measures in the community for internet addiction. Clin. Psychiatry 2017; 59: 53-59.
  23. Khan MA, Shabbir F, Rajput TA. Effect of gender and physical activity on internet addiction in medical students. Pak. J. Med. Sci. 2017; 33: 191-194.
  24. Ko CH, Yen JY, Chen CC, Chen SH, Yen CF. Gender differences and related factors affecting online gaming addiction among Taiwanese ado- lescents. J. Nerv. Ment. Dis. 2005; 193: 273-277.
  25. Van den Bulck J. Television viewing, computer game playing, and inter- net use and self-reported time to bed and time out of bed in secondary- school children. Sleep 2004; 27: 101-104.
  26. Ikeda K, Nakamura K. Association between mobile phone use and depressed mood in Japanese adolescents: A cross-sectional study. Envi- ron. Health Prev. Med. 2014; 19: 187-193.
  27. Koyuncu T, Unsal A, Arslantas D. Assessment of internet addiction and loneliness in secondary and high school students. J. Pak. Med. Assoc. 2014; 64: 998-1002.
  28. Tobe H, Takeuchi K, Hotta M. The relationship between the tendency toward internet dependence and mental health and the psycho-social prob- lems of students. Jpn. J. School Health. 2010; 52: 125-134 (in Japanese).
  29. Ko CH, Wang PW, Liu TL, Yen CF, Chen CS, Yen JY. Bidirectional associations between family factors and internet addiction among adoles- cents in a prospective investigation. Psychiatry Clin. Neurosci. 2015; 69: 192-200.
  30. Park SK, Kim JY, Cho CB. Prevalence of internet addiction and correla- tions with family factors among South Korean adolescents. Adolescence 2008; 43: 895-909.
  31. van den Eijnden RJ, Spijkerman R, Vermulst AA, van Rooij TJ, Engels RC. Compulsive internet use among adolescents: Bidirectional parent-child relationships. J. Abnorm. Child Psychol. 2010; 38: 77-89.
  32. Yu L, Shek DT. Internet addiction in Hong Kong adolescents: A three- year longitudinal study. J. Pediatr. Adolesc. Gynecol. 2013; 26: S10-S17.

Cited by

Sorted by citations

Authors
Kojima, R., Sato, M., Akiyama, Y., Shinohara, R., Mizorogi, S., Suzuki, K., Yokomichi, H., & Yamagata, Z.
Affiliations
  • University of Yamanashi (JP)
  • Aichi Medical University (JP)
Corresponding author
Kojima, Reiji
ORCID
Venue
Psychiatry and Clinical Neurosciences
Publisher
Wiley
Published
2018-12-04
Volume
73
Issue
1
Pages
20-26
ISSN
  • 1323-1316 (ISSN-L)
  • 1440-1819
DOI
10.1111/pcn.12791
OpenAlex
W2899057053
Cited by
70
References
5
Citations by year
2025: 8 · 2024: 10 · 2023: 7 · 2022: 13 · 2021: 15 · 2020: 14 · 2019: 3
FWCI
18.20
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Psychiatry and Clinical Neurosciences (publishedVersion)
  • PubMed (publishedVersion)
Funders
Ministry of Health, Labour and Welfare (H28‐sukoyaka‐ippan‐001)
Topics
  • Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
  • Social Sciences › Education › Child Development and Digital Technology
Keywords
problematic internet use, internet addiction, adolescents, rural youth, lifestyle habits, sleep duration, bedtime, breakfast skipping, exercise habits, study time, depressive symptoms, orthostatic dysregulation, prevalence trends, junior high school students
MeSH terms
Adolescent, Adolescent Behavior, Cross-Sectional Studies, Depression, Female, Health Status, Health Surveys, Humans, Japan, Life Style, Male, Rural Population, Students, Prevalence, Behavior, Addictive, Internet

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review