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Six addiction components of problematic social media use in relation to depression, anxiety, and stress symptoms: a latent profile analysis and network analysis

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Peng, Pu, Liao, Yanhui·2023·BMC Psychiatry

DOI 10.1186/s12888-023-04837-2PMC PMC10166459

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Abstract

BACKGROUNDS: Components of addiction (salience, tolerance, mood modification, relapse, withdrawal, and conflict) is the most cited theoretical framework for problematic social media use (PSMU). However, studies criticized its ability to distinguish problematic users from engaged users. We aimed to assess the association of the six criteria with depression, anxiety, and stress at a symptom level. METHODS: Ten thousand six hundred sixty-eight participants were recruited. Bergen Social Media Addiction Scale (BSMAS) was used to detect six addiction components in PSMU. We applied the depression-anxiety-stress scale to assess mental distress. Latent profile analysis (LPA) was conducted based on BSMAS items. Network analysis (NA) was performed to determine the symptom-symptom interaction of PSMU and mental distress. RESULTS: (1) Social media users were divided into five subgroups including occasional users (10.6%, n = 1127), regular users (31.0%, n = 3309), high engagement low risk users (10.4%, n = 1115), at-risk users (38.1%, n = 4070), and problematic users (9.8%, n = 1047); (2) PSMU and mental distress varied markedly across subgroups. Problematic users had the most severe PSMU, depression, anxiety, and stress symptoms. High engagement users scored high on tolerance and salience criteria of PSMU but displayed little mental distress; (3) NA showed conflict and mood modification was the bridge symptoms across the network, while salience and tolerance exhibited weak association with mental distress. CONCLUSIONS: Salience and tolerance might not distinguish engaged users from problematic users. New frameworks and assessment tools focusing on the negative consequences of social media usage are needed.

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Fig. 1 The five subgroups of social media users. X-axis represents the BSAMS items. Y-axis represents the scores of each BSAMS itemCC BY · PubMed Central

Fig. 2 The network analysis of PSMU, depression, anxiety, and stress in at-risk users and problematic users. Orange, blue, green, and yellow nodes represent PSMU, stress, anxiety, and depression symptoms, respectively. Blue and red edges indicate positive and negative associations. Thicker edges suggest stronger associationsCC BY · PubMed Central

Fig. 3 The centrality plot and bridge expected influence plot of the network. a The X-rays represented the expected influence of each node. Nodes with higher expected influence have stronger impact in other nodes within the network. b The X-rays represented the bridge expected influence of each node. Nodes with higher bridge expected influence are recognized as bridge symptoms that drove the comorbid depression and anxiety symptomsCC BY · PubMed Central

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Authors: Peng, P., & Liao, Y.
Affiliations: Sir Run Run Shaw Hospital (CN)
Corresponding author: Liao, Yanhui
ORCID: Peng, Pu
Liao, Yanhui
Venue: BMC Psychiatry
Publisher: Springer Science and Business Media LLC
Published: 2023-05-08
Volume: 23
Issue: 1
Pages: 321-321
ISSN: 1471-244X (ISSN-L)
DOI: 10.1186/s12888-023-04837-2
PMC: PMC10166459
OpenAlex: W4375955897
Cited by: 114
References: 53
Citations by year: 2026: 51 · 2025: 37 · 2024: 17 · 2023: 9
FWCI: 48.97
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: BMC Psychiatry (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: STI 2030—Major Projects - "Brain Science and Brain-like Research" Project (2022ZD0211200)
Topics: Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
Social Sciences › Psychology › Clinical Psychology › Sexuality, Behavior, and Technology
Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords: social media addiction, salience, tolerance, mood modification, relapse, withdrawal, conflict, depression, anxiety, stress, Bergen Social Media Addiction Scale, symptom networks
MeSH terms: Anxiety, Anxiety Disorders, Depression, Humans, Mood Disorders, Social Media

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Six addiction components of problematic social media use in relation to depression, anxiety, and stress symptoms: a latent profile analysis and network analysis

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