A study on the impact and buffer path of the internet use gap on population health

Latent category analysis and mediating effect analysis

Datos Bibliográficos

ID22067063
AutoresYuanyuan He (0000-0003-4762-6724, Jiangsu University), Lulin Zhou (0000-0002-5266-2191, Jiangsu University, autor de correspondencia), Xinglong Xu (0000-0002-2142-9442, Jiangsu University), Junshan Li (0000-0002-1482-1972, Jiangxi University of Traditional Chinese Medicine), Jiaxing Li (0009-0004-3205-7041, Jiangxi University of Traditional Chinese Medicine)
Año2022
Volumen10
Páginas958834-958834
Fecha de publicación2022-11-02
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Public Health (JOURNAL)
Identificadores de la revistaISSN: 2296-2565 • E-ISSN: 2296-2565
EditorialFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2022.958834
PMID36407977
OpenAlexW4308122650
IdiomaEN
Citas recibidas1
Referencias citadas28

Background The development of Internet information technology will generate an Internet use gap, which will have certain adverse effects on health, but internet information dependence can alleviate these negative effects. Objective This article is to demonstrate the negative impact of the internet use gap on population health in developing countries and to propose improvement paths. Methods This article used the 2018 China Family Tracking Survey database ( N = 11086). The research first used Latent class analysis (LCA) to identify potential categories of users with different Internet usage situations, then used the Bolck, Croon, and Hagenaars (BCH) method to perform latent class modeling with a continuous distal outcome, and finally built an intermediary model about Internet information dependence based on the model constraint function in Mplus software. Results (1) The Internet users can be divided into light-life users (C1: N = 1,061, 9.57%), all-around users ( N = 1,980, 17.86%(C2: N = 1,980, 17.86%), functional users (C3: N = 1,239, 11.18%), and pure-life users (C4: N = 6,806, 61.39%). (2) We examined individual characteristics, social characteristics and different living habits, and health differences between the latent classes. For example, there are certain structural differences on the effect of different categories of Internet use on health (C1: M = 3.089, SE = 0.040; C2: M = 3.151, SE = 0.037; C3: M = 3.070, SE = 0.035; C4: M = 2.948, SE = 0.016; P < 0.001). (3) The Internet use gap can affect health through the indirect path of Internet information dependence, and some of the mediation effects are significant. When the functional user group (C3) was taken as the reference group, the mediating effect values of light-life users (C1) and all-around users (C4) on health were −0.050 (SE = 0.18, Est./SE = −3.264, P = 0.001) and −0.080 (SE = 0.010, Est./SE = −8.412, P = 0.000) through Internet information dependence, respectively. However, the effect of categories on health was not significant after adding indirect paths. Conclusion The Internet use gap has a significant effect on health, and Internet information dependence plays an intermediary role in this effect path. The study proposes that attention should be paid to the diversified development of Internet use, the positive guiding function of Internet information channels should be made good use of, and the countermeasures and suggestions of marginalized groups in the digital age should also be paid attention to and protected

Internet privacy · Latent class model · Machine learning · Structural equation modeling · The Internet · World Wide Web · Computer Science · Health Literacy and Information Accessibility · Impact of Technology on Adolescents · Medicine · Technology Use by Older Adults