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Record W3048780474 · doi:10.1111/apa.15528

Excessive recreational Internet use was associated with poor mental health in adolescents

2020· article· en· W3048780474 on OpenAlexaff
Asaduzzaman Khan, Riaz Uddin, Eun‐Young Lee

Bibliographic record

VenueActa Paediatrica · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineRecreationOdds ratioMental healthAnxietyDemographyDepression (economics)PsychosocialOddsThe InternetPsychological interventionLogistic regressionConfidence intervalEnvironmental healthSocial mediaPsychiatry

Abstract

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Adolescents spend significant time using the Internet for recreational and academic purposes. It can increase social interaction and peer-to-peer support, but excessive use has been linked to psychosocial problems, including hyperactivity, anxiety and depression, sleep problems and self-harm.1, 2 Nearly every adolescent in South Korea has access to the Internet, and studies from 2003 to 2009 showed that higher recreational Internet use was associated with poor mental health.3, 4 This study examined the Korea Youth Risk Behavior Web-based Survey from 2011 to 2016. This is a repeated cross-sectional and nationally representative multistage cluster sampled survey of adolescents aged 12-18. About 800 schools take part each year. Students completed a web-based survey and rated how often they felt stressed on regular days, based on a five-point scale. We dichotomised the responses into 1-2 for above average stress and 3-5 for average or below average stress. Participants also reported whether they had felt sadness or despair during the past 12 months to the extent that it affected their daily routines. They reported their Internet use for recreational and academic purposes on weekdays and weekends. We computed total Internet use for recreational use on an average day and divided participants into three groups: low use of <2 hours per day, moderate use of 2-3 hours and high use of 4 hours plus. We conducted logistic regression, stratified by gender for each survey year, to estimate the odds ratios and 95% confidence intervals of the association estimates, adjusted for age, body mass index, family economic status and academic Internet use. The regression analyses were weighted to account for selection probabilities, survey non-responses, post-stratification and the multistage cluster sampling design. We used meta-analysis with random effects to derive overall pooled estimates. Between 2011 and 2016, the average response rate to the survey was 96.4% and 330 080 adolescents (53.4% boys), with a mean age of 14.8 ± 1.6 years, were included in the analyses. Overall, 31.8% of boys and 45.8% of girls reported stress and 22.6% and 32.7% reported sadness. Moderate Internet use was reported by 39.3% of boys and 32.5% of girls, while high Internet use was 14.3% and 11.2% (Figure S1). In general, high stress and sadness increased in a linear fashion as adolescents spent more time online. For the low, moderate and high user groups, it was 29.9%, 32.5% and 38.5% for the boys and 43.1%, 48.5% and 55.2% for girls. For sadness, the respective figures were 21.2%, 23.3% and 26.1% for the boys and 30.2%, 35.1% and 41.1% for the girls. Boys in the high user group had 36% higher odds of reporting high stress and 24% higher odds of reporting sadness than the low user group (Figure 1). The figures for the girls were 56% and 61%. The odds of reporting stress and sadness were 20% and 23% more for girls with moderate than low use. This large, nationally representative Korean survey showed that from 2011 to 2016 adolescents with high recreational Internet use had considerably higher odds of reporting stress and sadness than those who used the Internet for <2 hours per day. The association estimates were relatively higher for girls than boys. Even moderate Internet use was associated with high odds of stress or sadness for girls. These high levels of stress and sadness were consistent with previous studies.1-4 Increased Internet use can reduce opportunities for real-life social interaction 3 and promote overall screen time, which can adversely affect adolescents’ well-being.1, 2 High Internet use can also adversely affect sleep, which may increase psychosocial vulnerability.1 Social media use has been associated with anxiety and depression in adolescents, which can lead to stress and, or, sadness.5 However, the negative association between Internet use and stress or sadness was stronger for our girls, which was consistent with other findings.2 Girls may be more vulnerable to internalising problems, which can increase anxiety and depression, and low self-esteem, which can make them more susceptible to stress and sadness.2 Negative body image, body dissatisfaction and self-objectification, resulting from high social media use, can be more pronounced in girls. Therefore, policies and initiatives that make females feel more positive about themselves are needed to reduce poor mental health related to Internet use. The data were adjusted for various potential confounders. Self-reported data are vulnerable to bias. Our findings do not suggest causality, and the relationships could be bidirectional. For example, adolescents with pre-existing conditions, including social isolation or withdrawal, use the Internet more3 and some adolescents spend more time using the Internet to connect with friends and family when they are sad or stressed. Our findings add to the growing links between adolescents’ recreational Internet use and poor mental health. Parents, teachers and other stakeholders can help adolescents to restrict their usage, which can improve their well-being. Longitudinal studies are needed to understand the causal pathways and directions of the associations. We thank the Korea Centers for Disease Control and Prevention, the Ministry of Education, and the Ministry of Health and Welfare for providing the datasets. RU is supported by Alfred Deakin Post Doctoral Research Fellowship. None. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.289
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations12
Published2020
Admission routes1
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