Flattening the Mental Health Curve: COVID-19 Stay-at-Home Orders Are Associated With Alterations in Mental Health Search Behavior in the United States (Preprint)
Open accessPreprint — published as 10.2196/19347
Abstract
BACKGROUND: The coronavirus disease (COVID-19) has led to dramatic changes worldwide in people’s everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health. OBJECTIVE: The goal of this study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries on a national scale. METHODS: In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, 11 of the 50 states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the United States allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. This paper examines the changes in mental health search queries on Google between March 16-23, 2020, across each state and Washington, DC. Specifically, this paper examines differential changes in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. The participants were all the people who searched mental health terms in Google between March 16-23. Between March 16-23, 11 states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation. RESULTS: Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances, with the most prominent flattening associated with suicidal ideation and anxiety. CONCLUSIONS: These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediate leveling off following the issuance of stay-at-home orders.
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The steady decline conforms with similar findings in Google search queries as stay-at-home orders and other COVID-19-related policy changes were implemented in the United States.
“which conforms with similar findings in Google search queries as stay-at-home orders and other COVID-19–related policy changes were implemented in the United States [105].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [105]
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Cites
The psychological impact of quarantine and how to reduce it: Rapid review of the evidence
Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessely, S., Greenberg, N., & Rubin, G. J. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30460-8 · Cited by 16,378 · see citing works
Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17051729 · Cited by 9,923 · see citing works
Diagnostic and Statistical Manual of Mental Disorders (DSM-IV)
Hu, R. J. (2003) · Elsevier eBooks · doi:10.1016/b0-12-226870-9/01070-4 · Cited by 9,167 · see citing works
Lai, C.-C., Shih, T.-P., Ko, W.-C., Tang, H.-J., & Hsueh, P.-R. (2020) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2020.105924 · Cited by 6,068 · see citing works
Covid-19 — Navigating the Uncharted
Fauci, A. S., Lane, H. C., & Redfield, R. (2020) · New England Journal of Medicine · doi:10.1056/nejme2002387 · Cited by 1,983 · see citing works
SARS control and psychological effects of quarantine, Toronto, Canada
Hawryluck, L., Gold, W. L., Robinson, S., Pogorski, S., Galea, S., & Styra, R. (2004) · Emerging Infectious Diseases · doi:10.3201/eid1007.030703 · Cited by 1,929 · see citing works
Scientific and ethical basis for social-distancing interventions against COVID-19
Lewnard, J. A., & Lo, N. C. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30190-0 · Cited by 957 · see citing works
Public health interventions and epidemic intensity during the 1918 influenza pandemic
Hatchett, R. J., Mecher, C. E., & Lipsitch, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610941104 · Cited by 778 · see citing works
American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders (DSM-IV)
(2012) · SpringerReference · doi:10.1007/springerreference_179660 · Cited by 527 · see citing works
Prespecified Falsification End Points
Prasad, V. K., & Jena, A. B. (2013) · JAMA · doi:10.1001/jama.2012.96867 · Cited by 284 · see citing works
Huang, Y., & Zhao, N. (2020) · medRxiv · doi:10.1101/2020.02.19.20025395 · Cited by 238 · see citing works
Wang, Y., Xu, B., Zhao, G., Cao, R., He, X., & Fu, S. (2010) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2010.11.001 · Cited by 206 · see citing works
The impact of uncertainty and communal coping on mental health following natural disasters
Afifi, W. A., Felix, E. D., & Afifi, T. D. (2011) · Anxiety Stress & Coping · doi:10.1080/10615806.2011.603048 · Cited by 203 · see citing works
Internet monitoring of suicide risk in the population
McCarthy, M. J. (2009) · Journal of Affective Disorders · doi:10.1016/j.jad.2009.08.015 · Cited by 156 · see citing works
Using google searches on the internet to monitor suicidal behavior
Gunn, J. F., & Lester, D. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2012.11.004 · Cited by 127 · see citing works
Barros, J. M., Melia, R., Francis, K., Bogue, J., O’Sullivan, M., Young, K. L., Bernert, R. A., Rebholz‐Schuhmann, D., & Duggan, J. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16173201 · Cited by 72 · see citing works
Development and initial implementation of the Dynamic Assessment Treatment Algorithm (DATA)
Fernandez, K. C., Fisher, A. J., & Chi, C. (2017) · PLOS ONE · doi:10.1371/journal.pone.0178806 · Cited by 68 · see citing works
Sueki, H. (2011) · Psychiatry and Clinical Neurosciences · doi:10.1111/j.1440-1819.2011.02216.x · Cited by 63 · see citing works
Estimating suicide occurrence statistics using Google Trends
Krištoufek, L., Moat, H. S., & Preis, T. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0094-0 · Cited by 58 · see citing works
Forecasting state-level premature deaths from alcohol, drugs, and suicides using Google Trends data
Parker, J., Cuthbertson, C. A., Loveridge, S., Skidmore, M. L., & Dyar, W. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.10.038 · Cited by 41 · see citing works
Lee, J.-Y. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.11.014 · Cited by 40 · see citing works
Seasonality Patterns of Internet Searches on Mental Health: Exploratory Infodemiology Study
Soreni, N., Cameron, D. H., Streiner, D. L., Rowa, K., & McCabe, R. E. (2019) · JMIR Mental Health · doi:10.2196/12974 · Cited by 38 · see citing works
Physics: The Next 1 000 000 000 000 000 000 000 000 000 000 Years
Wilkinson, D. (1982) · Physics Bulletin · doi:10.1088/0031-9112/33/4/033 · Cited by 17 · see citing works
Waszak, P. M., Gorski, P., Springer, J., Kasprzycka-Waszak, W., Duzy, M., & Zagożdżon, P. (2018) · Psychiatria Danubina · doi:10.24869/psyd.2018.404 · Cited by 8 · see citing works
Harries, A., & Takarinda, K. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.737524760.793572567 · Cited by 7 · see citing works
Guelachvili, G., & Picqué, N. (2013) · Springer eBooks · doi:10.1007/978-3-642-32188-7_82 · Cited by 5 · see citing works
Various literature-derived datasets analysed in the manuscript
Molenaar, A. D. J. J. (2017) · doi:10.7717/peerj.3197/supp-2 · Cited by 1
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Huckins, J. F., daSilva, A. W., Wang, W., Hedlund, E., Rogers, C., Nepal, S. K., Wu, J., Obuchi, M., Murphy, E. I., Meyer, M. L., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/20185 · Cited by 759 · see citing works
Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media
Saha, K., Torous, J., Caine, E. D., & De Choudhury, M. (2020) · Journal of Medical Internet Research · doi:10.2196/22600 · Cited by 166 · see citing works
Castaldelli-Maia, J. M., Marziali, M. E., Lu, Z., & Martins, S. S. (2021) · Psychological Medicine · doi:10.1017/s0033291721000933 · Cited by 134 · see citing works
Nanath, K., Balasubramanian, S., Shukla, V., Islam, N., & Kaitheri, S. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121560 · Cited by 43 · see citing works
“This is in line with previous studies that reported the adverse effects of stay-at-home orders (Jacobson et al., 2020) and confining daily activities to close to home (Townley et al., 2009).”
Elbarazi, A. (2025) · Journal of Public Health Research · doi:10.1177/22799036251347030 · Cited by 4 · see citing works
- Authors
- Jacobson, N. C., Lekkas, D., Price, G., Heinz, M. V., Song, M., O’Malley, A. J., & Barr, P. J.
- Affiliations
- Dartmouth College (US)
- Corresponding author
- Jacobson, Nicholas C
- ORCID
- Venue
- JMIR Mental Health
- Publisher
- JMIR Publications Inc.
- Published
- 2020-04-14
- Volume
- 7
- Issue
- 6
- Pages
- e19347-e19347
- ISSN
- 2368-7959 (ISSN-L)
- PMC
- PMC7265799
- OpenAlex
- W3032334321
- Cited by
- 195
- References
- 26
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 13 · 2023: 24 · 2022: 43 · 2021: 69 · 2020: 36
- FWCI
- 17.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Mental Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Funders
- National Institute on Drug Abuse (P30DA029926)
- Topics
- Social Sciences › Psychology › Clinical Psychology › COVID-19 and Mental Health
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- COVID-19 pandemic, stay-at-home orders, mental health, web search queries, Google search data, suicidal ideation, anxiety, depression, sleep disorders, social distancing, quarantine, psychiatric symptoms
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