J Korean Med Sci.  2022 Apr;37(14):e108. 10.3346/jkms.2022.37.e108.

Trends of Internet Search Volumes for Major Depressive Disorder Symptoms During the COVID-19 Pandemic in Korea: An Interrupted Time-Series Analysis

• KMID: 2528140
• DOI: http://doi.org/10.3346/jkms.2022.37.e108

Abstract

Background
The effect of coronavirus disease 2019 (COVID-19) pandemic on public mental health has been increasing. Additionally, the underlying psychological stressors remain unexplored, and few studies have been conducted nationally on the social distancing measures. Therefore, the present study aimed to identify the psychological impacts of the implementation of social distancing measures by analyzing the Internet search trends of major depressive disorder (MDD) symptoms.
Methods
Using Naver ^® Trends’ relative search volumes (RSVs), we analyzed the average search volumes and trend changes of 16 terms, adopted from the Diagnostic and Statistical Manual of Mental Disorders-5 criteria for diagnosing MDD. An interrupted time-series analysis was performed using the data from January 1, 2016 to December 31, 2020. Furthermore, changes in RSVs, according to the intensity of the social distancing measures implemented from January 1 to December 31, 2020, were determined using Wilcoxon rank sum tests.
Results
Of the 16 terms, the search trends of ‘feeling guilty’ (P < 0.001) and ‘wanting to die’ (P = 0.002) showed a significant increase as of February 29, 2020, when the social distancing measures were officially implemented. Additionally, the average search volumes for ‘hopelessness’ (P = 0.003), ‘sexual desire’ (P < 0.001), ‘insomnia’ (P = 0.002), ‘hypersomnia’ (P < 0.001), ‘restlessness’ (P < 0.001), and ‘feeling lethargic’ (P < 0.001) increased significantly. Moreover, gender analysis showed that the average search volume of ‘depressed mood’ (P = 0.033) and the trend of ‘impaired concentration’ (P < 0.001) increased in males only. However, the average search volume of ‘feeling lethargic’ (P = 0.001) and the trend of ‘feeling guilty’ (P = 0.001) increased in females only. The average search volumes for ‘depressed mood’ (P < 0.001), ‘impaired concentration’ (P = 0.038), and ‘indecisiveness’ (P < 0.001) were significantly higher during the enforcement of level 2 or higher social distancing measures.
Conclusion
Our results reported the negative effect of COVID-19 preventive measures on public mental health in South Korea, especially for MDD symptoms. Moreover, the findings suggested the utility of Naver Trends as a feasible big data source to assess future mental health crises.

Keyword

COVID-19; Physical Distancing; Major Depressive Disorder; Interrupted TimeSeries Analysis

Reference

1. Dighe A, Cattarino L, Cuomo-Dannenburg G, Skarp J, Imai N, Bhatia S, et al. Response to COVID-19 in South Korea and implications for lifting stringent interventions. BMC Med. 2020; 18(1):321. PMID: 33032601.

2. Choi S, Ki M. Analyzing the effects of social distancing on the COVID-19 pandemic in Korea using mathematical modeling. Epidemiol Health. 2020; 42:e2020064. PMID: 32898942.

3. Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet. 2020; 395(10227):912–920. PMID: 32112714.

4. Wang Y, Shi L, Que J, Lu Q, Liu L, Lu Z, et al. The impact of quarantine on mental health status among general population in China during the COVID-19 pandemic. Mol Psychiatry. 2021; 26(9):4813–4822. PMID: 33483692.

5. Pfefferbaum B, North CS. Mental health and the COVID-19 pandemic. N Engl J Med. 2020; 383(6):510–512. PMID: 32283003.

6. Culpepper L, Muskin PR, Stahl SM. Major depressive disorder: understanding the significance of residual symptoms and balancing efficacy with tolerability. Am J Med. 2015; 128(9):Suppl. S1–15. PMID: 26337210.

7. Saragoussi D, Christensen MC, Hammer-Helmich L, Rive B, Touya M, Haro JM. Long-term follow-up on health-related quality of life in major depressive disorder: a 2-year European cohort study. Neuropsychiatr Dis Treat. 2018; 14:1339–1350. PMID: 29872301.

8. Ettman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S. Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA Netw Open. 2020; 3(9):e2019686. PMID: 32876685.

9. Ha SK, Lee HS, Park HY. Impact of COVID-19 on individual depression and quality of life: focusing on differences by age group. Ther Sci Rehabil. 2021; 10(3):111–122.

10. Seong G, Kim S. The impact of changes in daily life due to COVID-19 on corona-blue. Couns Psychol Educ Welf. 2021; 8(5):67–79.

11. Alsolais A, Alquwez N, Alotaibi KA, Alqarni AS, Almalki M, Alsolami F, et al. Risk perceptions, fear, depression, anxiety, stress and coping among Saudi nursing students during the COVID-19 pandemic. J Ment Health. 2021; 30(2):194–201. PMID: 33978543.

12. Suárez-Rico BV, Estrada-Gutierrez G, Sánchez-Martínez M, Perichart-Perera O, Rodríguez-Hernández C, González-Leyva C, et al. Prevalence of depression, anxiety, and perceived stress in postpartum mexican women during the COVID-19 lockdown. Int J Environ Res Public Health. 2021; 18(9):4627. PMID: 33925373.

13. Knipe D, Evans H, Marchant A, Gunnell D, John A. Mapping population mental health concerns related to COVID-19 and the consequences of physical distancing: a Google Trends analysis. Wellcome Open Res. 2020; 5:82. PMID: 32671230.

14. Stijelja S, Mishara BL. COVID-19 and psychological distress-changes in internet searches for mental health issues in New York during the pandemic. JAMA Intern Med. 2020; 180(12):1703–1706. PMID: 33016982.

15. Lin YH, Chiang TW, Lin YL. Increased internet searches for insomnia as an indicator of global mental health during the COVID-19 pandemic: multinational longitudinal study. J Med Internet Res. 2020; 22(9):e22181. PMID: 32924951.

16. Knipe D, Gunnell D, Evans H, John A, Fancourt D. Is Google Trends a useful tool for tracking mental and social distress during a public health emergency? A time-series analysis. J Affect Disord. 2021; 294:737–744. PMID: 34348169.

17. Internet Trend. Search engine. Updated 2022. Accessed January 18, 2022. http://www.internettrend.co.kr/trendForward.tsp .

18. American Psychiatric Association (APA). Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Washington, D.C.: APA;2013.

19. Statistics Korea. COVID-19 dashboard. Updated 2022. Accessed January 20, 2021. https://kosis.kr/covid_eng/covid_index.do .

20. Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol. 2017; 46(1):348–355. PMID: 27283160.

21. Ministry of Health and Welfare (KR). 2021 mental health survey results announcement. Updated 2021. Accessed January 18, 2022. http://www.mohw.go.kr/react/modules/download.jsp?BOARD_ID=140&CONT_SEQ=369088&FILE_SEQ=332317 .

22. Silver L. Smartphone ownership is growing rapidly around the world, but not always equally: pew research center. Updated 2019. Accessed January 18, 2022. https://www.pewresearch.org/global/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/ .

23. Massaro M, Kim S. Why is South Korea at the forefront of 5G? Insights from technology systems theory. Telecomm Policy. 2021; 102290.

24. Kang M, Zhong H, He J, Rutherford S, Yang F. Using Google Trends for influenza surveillance in South China. PLoS One. 2013; 8(1):e55205. PMID: 23372837.

25. Senecal C, Mahowald M, Lerman L, Lopes-Jimenez F, Lerman A. Increasing utility of Google Trends in monitoring cardiovascular disease. Digit Health. 2021; 7:20552076211033420. PMID: 34873449.

26. Barros JM, Melia R, Francis K, Bogue J, O’Sullivan M, Young K, et al. The validity of Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland. Int J Environ Res Public Health. 2019; 16(17):3201.