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Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Apr 19, 2021)

Date Submitted: Jan 17, 2021

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Anxiety and its relationship with sleep disturbance and smartphone addition among Chinese medical students during COVID-19 home confinement — A Structural Equation Model Analysis

  • Yang Song; 
  • Kristin Sznajder; 
  • Can Cui; 
  • Yajing Jia; 
  • Yilong Yang; 
  • Ying Li; 
  • Xiaoshi Yang

ABSTRACT

Background:

Medical students, the majority of whom have experienced home confinement and a reliance on online resources to study medicine since the outbreak of COVID-19, have been found to suffer increased level of anxiety. Since smartphones have become the most commonly used device for accessing the internet, more time spent studying online during the COVID pandemic may be a potential risk factor for unconscious smartphone addiction, which could lead to elevated levels of sleep disturbance and anxiety. However, there is a dearth of research examining anxiety and its relationship with sleep disturbance and smartphone addition among medical students during the COVID-19 pandemic. Further, no existing literature has examined sleep disturbance as a mediator in the relationship between smartphone addiction and anxiety.

Objective:

This study aimed to assess the association between anxiety, smartphone addiction and sleep disturbance among medical students during COVID-19 home confinement and to examine the mediating role of sleep disturbance between smartphone addiction and anxiety.

Methods:

This cross-sectional study was performed among medical students attending a university located in the northeast of China in April 2020, when all universities in China began to deliver live-streaming courses via various online platforms as part of the national COVID-19 prevention strategy (Ministry of Education of China, 2020). Altogether, 666 medical students validly answered a self-administered questionnaire, which included demographic information, the Chinese version of the Generalized Anxiety Disorder 7-item scale, Smartphone addiction scale - short version, and PROMIS Sleep Disturbance (short form). Hierarchical multiple regression analyses were employed to determine the extent to which each involved independent variable could contribute to the variance of anxiety. Structural equation modeling was conducted to test whether the hypothesized associations existed.

Results:

Anxiety exhibited robust positive associations with both smartphone addiction and sleep disturbance among medical students during the COVID-19 pandemic. Smartphone addiction not only directly affected anxiety but also exerted a significant indirect effect on anxiety via sleep disturbance. A significant decrease of the path coefficient of smartphone addiction on anxiety (from β=0.53 to β=0.22, P< .01) was observed with sleep disturbance being modeled as a mediator.

Conclusions:

This study demonstrated a robust positive association between anxiety, smartphone addiction, and sleep disturbance among medical students during COVID-19 home confinement. Smartphone addiction exerted not only a direct effect on anxiety but also an indirect impact on anxiety through the mediating effect of sleep disturbance. Therefore, professional assistance and timely intervention should be provided to alleviate anxiety among medical students during the COVID-19 pandemic. The detrimental impact of smartphone addiction and the importance of sleep health on mitigating anxiety should be highlighted and incorporated into medical education.


 Citation

Please cite as:

Song Y, Sznajder K, Cui C, Jia Y, Yang Y, Li Y, Yang X

Anxiety and its relationship with sleep disturbance and smartphone addition among Chinese medical students during COVID-19 home confinement — A Structural Equation Model Analysis

JMIR Preprints. 17/01/2021:27229

DOI: 10.2196/preprints.27229

URL: https://preprints.jmir.org/preprint/27229

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