Evidence map›Paper›PMID 32705394›Full record

ArticleJournal of autism and developmental disorders2021

Neurodevelopmental Traits and Longitudinal Transition Patterns in Internet Addiction: A 2-year Prospective Study.

Tomoya Hirota, Michio Takahashi, Masaki Adachi, Yui Sakamoto, Kazuhiko Nakamura

Abstract read
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In one paragraph

Article in Journal of autism and developmental disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

5 authors.

Tomoya HirotaDepartment of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, 401 Parnassus Avenue, San Francisco, CA, 94143, USA. tomoya.hirota123@gmail.com.ORCID http://orcid.org/0000-0002-2101-2655
Michio TakahashiGraduate School of Health Sciences, Hirosaki University, 5 Zaifu-cho, Hirosaki, Aomori, 036-8562, Japan.
Masaki AdachiGraduate School of Health Sciences, Hirosaki University, 5 Zaifu-cho, Hirosaki, Aomori, 036-8562, Japan.
Yui SakamotoDepartment of Neuropsychiatry, Graduate School of Medicine, Hirosaki University, 5 Zaifu-cho, Hirosaki, Aomori, 036-8562, Japan.
Kazuhiko NakamuraDepartment of Neuropsychiatry, Graduate School of Medicine, Hirosaki University, 5 Zaifu-cho, Hirosaki, Aomori, 036-8562, Japan.

Funding

Japan Society for the Promotion of Science 19K08008
6 · The paper itself

Abstract

Despite increasing attention to internet addiction (IA) in both clinical practice and research, our understanding of longitudinal changes of IA status is limited. In the present study, we employed latent transition analysis to investigate patterns of transitions and the stability of IA status among 5483 students (aged 9-12 years) over the two-year study periods. Additionally, we examined whether neurodevelopmental traits predicted certain transition patterns. The stability rate of IA class membership and the conversion rate from non-IA to IA status across the 2 years were 47% and 11%, respectively. The regression model revealed that autistic traits predicted the persisting IA pattern and that inattention traits predicted both the persisting and converting (from non-IA to IA status) patterns.

Indexed as

Child DevelopmentAutism Spectrum DisorderBehavior, AddictiveChildFemaleHumansInternet Addiction DisorderMaleNeurodevelopmental DisordersProspective StudiesStudentsInternet addictionLatent class analysisLatent transition analysisLongitudinal studyNeurodevelopmental traits

Identifiers

What OpenQuestion holds

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Registered trials

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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.