# The prevalence of internet addiction and its association with quality of life among inflight security officers based on a national survey: a network analysis perspective

## TL;DR

This study finds that internet addiction is common among flight security officers in China and is linked to lower quality of life, suggesting targeted interventions could help.

## Contribution

The study introduces a network analysis approach to identify central symptoms of internet addiction and their impact on quality of life in a specific occupational group.

## Key findings

- Internet addiction prevalence among IFSO was 13.1%, with lower income and mental health issues linked to higher risk.
- Network analysis identified key symptoms like job performance decline and preoccupation with the internet as central to internet addiction.
- Symptoms like sleep loss and social withdrawal were most harmful to quality of life, while forming online relationships was beneficial.

## Abstract

Given the heavy responsibilities placed on inflight security officers (IFSO) to ensure passenger safety and eliminate inflight hazards, they often turn to Internet use to cope with their work pressure. This study examined the prevalence of internet addiction (IA) among IFSO in China, and its associated factors, relationship with quality of life (QOL), and network structure.

This was a cross-sectional study based on a national survey. Expected influence (EI) was used to identify the most central nodes within the network model.

Among 3,475 IFSO included in this study across 10 airlines, the prevalence of IA (IAT-20 total score of ≥ 50) was 13.1% (n = 454; 95% CI: 11.9–14.2%). Further, there was an association between lower annual income, more severe depressive symptoms and anxiety symptoms with IA among IFSOs. Network analysis found that “Job performance or productivity suffer because of the Internet” (IAT8) was the most central symptom with the highest EI value, followed by “Preoccupation with the Internet” (IAT15) and “Depressed/moody/nervous only while being offline” (IAT20). Moreover, “Sleep loss due to late-night logins” (IAT14) and “Spend more time online over going out with others” (IAT19) had the most negative associations with QOL, while “Form new relationships with online users” (IAT4) showed the strongest positive correlation with QOL.

IA was common among IFSO. To reduce the adverse impact of IA among IFSO, appropriate interventions targeting central symptoms and those closely associated with QOL found in the network models should be developed.

The online version contains supplementary material available at 10.1007/s00406-025-02030-y.