Cargando…
Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis
BACKGROUND: As social media is a major channel of interpersonal communication in the digital age, social media addiction has emerged as a novel mental health issue that has raised considerable concerns among researchers, health professionals, policy makers, mass media, and the general public. OBJECT...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8787656/ https://www.ncbi.nlm.nih.gov/pubmed/35006084 http://dx.doi.org/10.2196/27000 |
_version_ | 1784639404823805952 |
---|---|
author | Cheng, Cecilia Ebrahimi, Omid V Luk, Jeremy W |
author_facet | Cheng, Cecilia Ebrahimi, Omid V Luk, Jeremy W |
author_sort | Cheng, Cecilia |
collection | PubMed |
description | BACKGROUND: As social media is a major channel of interpersonal communication in the digital age, social media addiction has emerged as a novel mental health issue that has raised considerable concerns among researchers, health professionals, policy makers, mass media, and the general public. OBJECTIVE: The aim of this study is to examine the prevalence of social media addiction derived from 4 major classification schemes (strict monothetic, strict polythetic, monothetic, and polythetic), with latent profiles embedded in the empirical data adopted as the benchmark for comparison. The extent of matching between the classification of each scheme and the actual data pattern was evaluated using sensitivity and specificity analyses. The associations between social media addiction and 2 comorbid mental health conditions—depression and anxiety—were investigated. METHODS: A cross-sectional web-based survey was conducted, and the replicability of findings was assessed in 2 independent samples comprising 573 adults from the United Kingdom (261/573, 45.6% men; mean age 43.62 years, SD 12.24 years) and 474 adults from the United States (224/474, 47.4% men; mean age 44.67 years, SD 12.99 years). The demographic characteristics of both samples were similar to those of their respective populations. RESULTS: The prevalence estimates of social media addiction varied across the classification schemes, ranging from 1% to 15% for the UK sample and 0% to 11% for the US sample. The latent profile analysis identified 3 latent groups for both samples: low-risk, at-risk, and high-risk. The sensitivity, specificity, and negative predictive values were high (83%-100%) for all classification schemes, except for the relatively lower sensitivity (73%-74%) for the polythetic scheme. However, the polythetic scheme had high positive predictive values (88%-94%), whereas such values were low (2%-43%) for the other 3 classification schemes. The group membership yielded by the polythetic scheme was largely consistent (95%-96%) with that of the benchmark. CONCLUSIONS: Among the classification schemes, the polythetic scheme is more well-balanced across all 4 indices. |
format | Online Article Text |
id | pubmed-8787656 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-87876562022-02-03 Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis Cheng, Cecilia Ebrahimi, Omid V Luk, Jeremy W J Med Internet Res Original Paper BACKGROUND: As social media is a major channel of interpersonal communication in the digital age, social media addiction has emerged as a novel mental health issue that has raised considerable concerns among researchers, health professionals, policy makers, mass media, and the general public. OBJECTIVE: The aim of this study is to examine the prevalence of social media addiction derived from 4 major classification schemes (strict monothetic, strict polythetic, monothetic, and polythetic), with latent profiles embedded in the empirical data adopted as the benchmark for comparison. The extent of matching between the classification of each scheme and the actual data pattern was evaluated using sensitivity and specificity analyses. The associations between social media addiction and 2 comorbid mental health conditions—depression and anxiety—were investigated. METHODS: A cross-sectional web-based survey was conducted, and the replicability of findings was assessed in 2 independent samples comprising 573 adults from the United Kingdom (261/573, 45.6% men; mean age 43.62 years, SD 12.24 years) and 474 adults from the United States (224/474, 47.4% men; mean age 44.67 years, SD 12.99 years). The demographic characteristics of both samples were similar to those of their respective populations. RESULTS: The prevalence estimates of social media addiction varied across the classification schemes, ranging from 1% to 15% for the UK sample and 0% to 11% for the US sample. The latent profile analysis identified 3 latent groups for both samples: low-risk, at-risk, and high-risk. The sensitivity, specificity, and negative predictive values were high (83%-100%) for all classification schemes, except for the relatively lower sensitivity (73%-74%) for the polythetic scheme. However, the polythetic scheme had high positive predictive values (88%-94%), whereas such values were low (2%-43%) for the other 3 classification schemes. The group membership yielded by the polythetic scheme was largely consistent (95%-96%) with that of the benchmark. CONCLUSIONS: Among the classification schemes, the polythetic scheme is more well-balanced across all 4 indices. JMIR Publications 2022-01-10 /pmc/articles/PMC8787656/ /pubmed/35006084 http://dx.doi.org/10.2196/27000 Text en ©Cecilia Cheng, Omid V Ebrahimi, Jeremy W Luk. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.01.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Cheng, Cecilia Ebrahimi, Omid V Luk, Jeremy W Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis |
title | Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis |
title_full | Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis |
title_fullStr | Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis |
title_full_unstemmed | Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis |
title_short | Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis |
title_sort | heterogeneity of prevalence of social media addiction across multiple classification schemes: latent profile analysis |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8787656/ https://www.ncbi.nlm.nih.gov/pubmed/35006084 http://dx.doi.org/10.2196/27000 |
work_keys_str_mv | AT chengcecilia heterogeneityofprevalenceofsocialmediaaddictionacrossmultipleclassificationschemeslatentprofileanalysis AT ebrahimiomidv heterogeneityofprevalenceofsocialmediaaddictionacrossmultipleclassificationschemeslatentprofileanalysis AT lukjeremyw heterogeneityofprevalenceofsocialmediaaddictionacrossmultipleclassificationschemeslatentprofileanalysis |