Applying network analysis to understand the relationships between impulsivity and social media addiction and between impulsivity and problematic smartphone use

Zhihua Guo; Shuyi Liang; Lei Ren; Tianqi Yang; Rui Qiu; Yang He; Xia Zhu

doi:10.3389/fpsyt.2022.993328

Applying network analysis to understand the relationships between impulsivity and social media addiction and between impulsivity and problematic smartphone use

Front Psychiatry. 2022 Oct 18:13:993328. doi: 10.3389/fpsyt.2022.993328. eCollection 2022.

Authors

Zhihua Guo¹, Shuyi Liang¹, Lei Ren¹, Tianqi Yang¹, Rui Qiu¹, Yang He¹, Xia Zhu¹

Affiliation

¹ Department of Military Medical Psychology, Air Force Medical University, Xi'an, China.

Abstract

Background: Prior studies have revealed the relationships between impulsivity and social media addiction (SMA) and between impulsivity and problematic smartphone use (PSU) based on total scores on standardized self-report scales. However, there has been a lack of studies exploring how the dimensions of impulsivity and components of SMA or PSU are interrelated. The present study aimed to investigate the structural relationships between the dimensions of impulsivity and components of SMA and PSU and determine the critical bridge node using network analysis.

Methods: A total of 325 healthy adults aged 18-36 years participated in the study. SMA and PSU were assessed using the Bergen Social Media Addiction Scale (BSMAS) and Smartphone Application-Based Addiction Scale (SABAS), respectively. Impulsivity was measured by the Barratt Impulsiveness Scale Version 11 (BIS-11). Network analysis was used to construct an SMA-Impulsivity network and a PSU-Impulsivity network. Bridge centrality (bridge expected influence, BEI) was estimated to identify influential bridge nodes.

Results: In addition to relationships within each community, network analysis revealed that the dimensions of impulsivity were closely associated with the components of SMA and PSU. Particularly, I2 "motor impulsivity" had a relatively strong connection with SMA3 "mood modification" and SMA4 "relapse" in the SMA-Impulsivity network, and with PSU2 "conflict" and PSU5 "withdrawal" in the PSU-Impulsivity network. Moreover, I2 "motor impulsivity" was identified as the most critical bridge node in both networks.

Conclusion: These findings demonstrate potential pathways between different dimensions of impulsivity and the components of SMA and PSU, providing new evidence relevant to understanding the underlying mechanisms that account for how highly impulsive individuals develop SMA and PSU, and highlight the critical bridge node-motor impulsivity-that may be a promising and effective target for the prevention and treatment of SMA and PSU.

Keywords: bridge node; impulsivity; network analysis; problematic smartphone use; social media addiction.