Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Oct 2:11:567484.
doi: 10.3389/fpsyg.2020.567484. eCollection 2020.

Mental Health Through the COVID-19 Quarantine: A Growth Curve Analysis on Italian Young Adults

Anna Parola  1 , Alessandro Rossi  2   3 , Francesca Tessitore  1 , Gina Troisi  1 , Stefania Mannarini  2   3
Affiliations

Mental Health Through the COVID-19 Quarantine: A Growth Curve Analysis on Italian Young Adults

Anna Parola et al. Front Psychol. .

Abstract

Introduction: Health emergencies, such as epidemics, have detrimental and long-lasting consequences on people's mental health, which are higher during the implementation of strict lockdown measures. Despite several recent psychological researches on the coronavirus disease 2019 (COVID-19) pandemic highlighting that young adults represent a high risk category, no studies specifically focused on young adults' mental health status have been carried out yet. This study aimed to assess and monitor Italian young adults' mental health status during the first 4 weeks of lockdown through the use of a longitudinal panel design.

Methods: Participants (n = 97) provided self-reports in four time intervals (1-week intervals) in 1 month. The Syndromic Scales of Adult Self-Report 18-59 were used to assess the internalizing problems (anxiety/depression, withdrawn, and somatic complaints), externalizing problems (aggressive, rule-breaking, and intrusive behavior), and personal strengths. To determine the time-varying effects of prolonged quarantine, a growth curve modeling will be performed.

Results: The results showed an increase in anxiety/depression, withdrawal, somatic complaints, aggressive behavior, rule-breaking behavior, and internalizing and externalizing problems and a decrease in intrusive behavior and personal strengths from T1 to T4.

Conclusions: The results contributed to the ongoing debate concerning the psychological impact of the COVID-19 emergency, helping to plan and develop efficient intervention projects able to take care of young adults' mental health in the long term.

Keywords: Achenbach adult self-report; coronavirus disease 2019; growth model; internalizing/externalizing problems; mental health; quarantine; young adult.

PubMed Disclaimer

Figures

FIGURE 1
FIGURE 1
Scatterplot of Syndromic Scales of the Adult Self-Report (ASR) for each week of quarantine.
FIGURE 2
FIGURE 2
Growth curve analysis: means and standard error of the Syndromic Scale across weeks of quarantine.
FIGURE 3
FIGURE 3
Growth curve analysis (GCA): means and standard error of the Syndromic Scale across weeks of quarantine split by sex.
FIGURE 4
FIGURE 4
Growth curve analysis (GCA): means and standard error of the Syndromic Scale across weeks of quarantine split by “experience of COVID-19”.
FIGURE 5
FIGURE 5
Growth curve analysis (GCA): means of the Syndromic Scale across weeks of quarantine—interaction between “sex” and “experience of COVID-19”.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Achenbach T. M., Rescorla L. A. (2003). Manual for the ASEBA Adult Forms & Profiles. Burlington: University of Vermont, Research Center for Children, Youth, and Families.
    1. Adams-Prassl A., Boneva T., Golin M., Rauh C. (2020). Inequality in the Impact of the coronavirus shock: evidence from real time surveys. Paper presented CEPR Discussion Paper No. DP14665. Schaumburg, IL: Institute of Labor Economics; 10.1016/j.jpubeco.2020.104245 - DOI
    1. Akaike H. (1973). Information theory and an extension of the maximum likelihood principle. Second International Symposium on Information Theory. Budapest: Akadémiai Kiadó.
    1. Anderson D. (2020). esvis: Visualization and Estimation of Effect Sizes. https://CRAN.R-project.org/package=esvis (accessed May 30, 2020).
    1. Azari R., Li L., Tsai C. -L. (2006). Longitudinal data model selection. Comp. Stat. Data Anal. 50(11), 3053–3066. 10.1016/j.csda.2005.05.009 - DOI

LinkOut - more resources