Evidence map›Paper›PMID 39478568›Full record

ArticleBMC psychology2024

Smartphone addiction, depression, distress, eustress, loneliness, and sleep deprivation in adolescents: a latent profile and network analysis approach.

Fedai Kabadayi

Abstract read
In one paragraph

Article in BMC psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–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

11 citing papers in PubMed.

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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

1 author.

Fedai KabadayiFaculty of Education, Department of Counseling and Guidance, Recep Tayyip Erdogan University, Rize, Türkiye. fdkbdy@gmail.com.ORCID http://orcid.org/0000-0003-2486-2337

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious research on adolescent smartphone addiction has tended to focus on general populations that are assumed to be homogeneous, overlooking latent profiles. Furthermore, previous research has not focused on potentially important differences in the latent profiles of adolescent smartphone addiction in networks. The present study aimed to reveal the latent profiles of smartphone addiction, depression, stress, eustress, loneliness, and sleep deprivation in adolescents, and general and latent profiles were examined in the network.

methodsThe study group consisted of 436 (222 boys and 214 girls) adolescents, aged between 13 and 18 years. The findings of the present study were provided using Pearson correlation, ANOVA, latent profile analysis and network analysis. The data were analyzed using SPSS, JASP, and Mplus.

resultsThe results of the study showed solutions with three latent profiles. The non-addicted group constituted 20.87%, the addicted group covered 29.82% and the risky group included 49.31% of the study group. Although the general profile and the addicted latent group had similar characteristics, the differences in the risky and non-addicted groups contributed to the current literature by providing a further and remarkable perspective on smartphone addiction, depression, distress, eustress, loneliness, and sleep deprivation in adolescents.

conclusionsThe theoretical and practical implications of the present results will provide contributions to researchers and practitioners in understanding smartphone addiction.

Indexed as

Adolescent BehaviorDepressionInternet Addiction DisorderLonelinessPsychological DistressSleep DeprivationAdolescentBehavior Rating ScaleCross-Sectional StudiesFemaleHumansLatent Class AnalysisMaleRisk-TakingSmartphoneSocial Network AnalysisDepressionLonelinessSleep deprivationSmartphone addictionStress

Identifiers

PMID39478568
PMCPMC11526649

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LicenceCC BY-NC-ND
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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.