Abstract
Purpose
Understanding people’s response to the pandemic needs to consider individual differences in priorities and concerns. The present study sought to understand how individual differences in cognitive-appraisal processes might moderate the impact of three COVID-specific factors—hardship, worry, and social support—on reported depression.
Methods
This longitudinal study of the psychosocial impact of the COVID-19 pandemic included 771 people with data at three timepoints over 15.5 months. Participants were recruited from panels of chronically ill or general population samples. Depression was measured by an item response theory validated depression index created using items from existing measures that reflected similar content to the Patient Health Questionnaire-8. COVID-specific factors of hardship, worry, and social support were assessed with items compiled by the National Institutes of Health. The Quality of Life Appraisal Profilev2 Short-Form assessed cognitive appraisal processes. A series of random effects models examined whether appraisal moderated the effects of hardship, worry, and social support on depression over time.
Results
Over time the association between low social support and depression was greater (p = 0.0181). Emphasizing the negative was associated with exacerbated depression, in particular for those with low social support (p = 0.0007). Focusing on demands and habituation was associated with exacerbated depression unless one experienced greater hardship (p = 0.0074). There was a stronger positive connection between recent changes and depression for those people with higher worry scores early in the pandemic as compared to later, but a stronger positive correlation for those with lower worry scores later in the pandemic (p = 0.0015). Increased endorsement of standards of comparison, emphasizing the negative, problem goals, and health goals was associated with worse depression scores (all p < 0.0001). People who were younger, disabled, or had greater difficulty paying bills also reported worse depression (p < 0.0001, 0.0001, and 0.002, respectively).
Conclusion
At the aggregate level, COVID-specific stressors changed over the course of the pandemic, whereas depression and social-support resources seemed stable. However, deeper analysis revealed substantial individual differences. Cognitive-appraisal processes showed considerable variability across individuals and moderated the impact of COVID-specific stressors and resources over time. Future work is needed to investigate whether coaching individuals away from maladaptive cognitive-appraisal processes can reduce depression and lead to better overall well-being.
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Data availability
The study data are confidential and thus not able to be shared.
Notes
To take into account that we are modeling the intersection of multiple individual-level predictors, i.e., the interaction between levels of appraisal scores, COVID-specific stressors, and time.
References
Rogers, T. N., Rogers, C. R., VanSant-Webb, E., Gu, L. Y., Yan, B., & Qeadan, F. (2020). Racial disparities in COVID-19 mortality among essential workers in the United States. World Medical & Health Policy, 12(3), 311–327.
Pan, D., Sze, S., & Minhas, J. S. (2020). The impact of ethnicity on clinical outcomes in COVID-19: A systematic review. EClinical Medicine, 23, 100404.
Ramos, A. K., Lowe, A. E., Herstein, J. J., Schwedhelm, S., Dineen, K. K., & Lowe, J. J. (2020). Invisible no more: The impact of COVID-19 on essential food production workers. Journal of agromedicine., 25(4), 378–382.
Ortolan, A., Lorenzin, M., Felicetti, M., Doria, A., & Ramonda, R. (2020). Does gender influence clinical expression and disease outcomes in COVID-19? A systematic review and meta-analysis. International Journal of Infectious Diseases., 99, 496–504.
Romero Starke, K., Petereit-Haack, G., & Schubert, M. (2020). The age-related risk of severe outcomes due to COVID-19 infection: A rapid review, meta-analysis, and meta-regression. International Journal of Environmental Research and Public Health., 17(16), 5974.
Rollston, R., & Galea, S. (2020). COVID-19 and the social determinants of health. American Journal of Health Promotion., 34(6), 687–689.
Betthäuser, B. A., Bach-Mortensen, A. M., & Engzell, P. A. (2023). systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nature Human Behaviour, 73, 1–11.
McLeod, S. A., Nomothetic idiographic debate. Updated February 5, 2019. www.simplypsychology.org/nomothetic-idiographic.html
Beck, S. J. (1953). The science of personality: Nomothetic or idiographic? Psychological Review., 60(6), 353.
Windelband, W. (1998). History and natural science (Original work published 1904). Theory & Psychology, 8, 5–22.
Hogben, L. (1993). Nature and nurture. W W Norton & Co.
Genes, behavior, and the social environment: Moving beyond the nature/nurture debate. National Academies Press; 2006: 368.
McEwen, B. S., & Getz, L. (2013). Lifetime experiences, the brain and personalized medicine: An integrative perspective. Metabolism, 62, S20–S26.
Cheung, Y. K., Hsueh, P.-Y.S., & Qian, M. (2017). Are nomothetic or ideographic approaches superior in predicting daily exercise behaviors? Methods of Information in Medicine, 56(06), 452–460.
Beltz, A. M., Wright, A. G., Sprague, B. N., & Molenaar, P. C. (2016). Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment, 23(4), 447–458.
Diener, E., & Fujita, F. (1995). Resources, personal strivings, and subjective well-being: A nomothetic and idiographic approach. Journal of Personality and Social Psychology, 68(5), 926.
Altman, A. D. (2022). An idiographic approach to assess the negative effects of Instagram on mental health. UC Berkeley
Ivie, E. J., Pettitt, A., Moses, L. J., & Allen, N. B. (2020). A meta-analysis of the association between adolescent social media use and depressive symptoms. Journal of Affective Disorders, 275, 165–174.
Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims (US Department of Health and Human Services Food and Drug Administration) (2009).
Schwartz, C. E., & Revicki, D. A. (2012). Mixing methods and blending paradigms: Some considerations for future research. Quality of Life Research, 21, 375–376.
Rapkin, B. D., & Schwartz, C. E. (2004). Toward a theoretical model of quality-of-life appraisal: Implications of findings from studies of response shift. Health and Quality of Life Outcomes, 2(1), 14.
Schwartz, C. E., & Rapkin, B. D. (2004). Reconsidering the psychometrics of quality of life assessment in light of response shift and appraisal. Health and Quality of Life Outcomes, 2, 16.
Tourangeau, R., Rips, L. J., & Rasinski, K. (2000). The psychology of survey response. Cambridge University Press.
Rapkin, B. D., & Schwartz, C. E. (2019). Advancing quality-of-life research by deepening our understanding of response shift: A unifying theory of appraisal. Quality of Life Research, 28(10), 2623–2630. https://doi.org/10.1007/s11136-019-02248-z
Rapkin, B. D., Garcia, I., Michael, W., Zhang, J., & Schwartz, C. E. (2017). Distinguishing appraisal and personality influences on quality of life in chronic illness: Introducing the Quality-of-Life Appraisal Profile version 2. Quality of Life Research, 26, 2815–2829. https://doi.org/10.1007/s11136-017-1600-y
Schwartz, C. E., Stark, R. B., & Rapkin, B. D. (2021). Creating idiometric short-form measures of cognitive appraisal: Balancing theory and pragmatics. Journal of Patient-Reported Outcomes, 5, 57. https://doi.org/10.1186/s41687-021-00317-x
Schwartz, C. E., Stark, R. B., & Rapkin, B. D. (2020). Capturing patient experience: Does quality-of-life appraisal entail a new class of measurement? Journal of Patient-Reported Outcomes, 4, 85. https://doi.org/10.1186/s41687-020-00254-1
Schwartz, C. E., Borowiec, K., & Rapkin, B. D. (2023). Depression trajectories during the COVID-19 pandemic: Impact of cognitive appraisal processes. Journal of Patient-Reported Outcomes, 7, 67. https://doi.org/10.1186/s41687-023-00600-z
Thomas, R. K. Documentation for human subject review committees: Ipsos company information, past external review, confidentiality, and privacy protections for panelists. 2019. August 7. https://www.ipsos.com/sites/default/files/Documentation%20for%20IRBs.pdf
Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B., Berry, J. T., & Mokdad, A. H. (2009). The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders, 114(1–3), 163–173.
Zimmerman, M., McGlinchey, J. B., Posternak, M. A., Friedman, M., Attiullah, N., & Boerescu, D. (2006). How should remission from depression be defined? The depressed patient’s perspective. American Journal of Psychiatry., 163(1), 148–150.
Hays, R. D., Bjorner, J. B., Revicki, D. A., Spritzer, K. L., & Cella, D. (2009). Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items. Quality of Life Research, 18, 873–880. https://doi.org/10.1007/s11136-009-9496-9
User Manual for the Quality of Life in Neurological Disorders (Neuro-QOL) Measures, version 2.0 (2015).
Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57, 1069–1081.
Power, M., Fell, G., & Wright, M. (2013). Principles for high-quality, high-value testing. BMJ Evidence-Based Medicine, 18(1), 5–10.
COVID-19 BSSR Research Tools (NIH Office of Behavioral and Social Sciences Research (OBSSR)) (2020).
Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
Holm, S. (1979). A simple sequentially rejective bonferroni test procedure. Scandinavian Journal of Statistics, 6, 65–70.
Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple significance testing. Biometrika, 75, 800–803.
Hochberg, Y., & Benjamini, Y. (1990). More powerful procedures for multiple significance testing. Statistics in Medicine., 9, 811–818.
G*Power Version 3.1.9.2. University Kiel; 1992–2014.
Kang, H. (2021). Sample size determination and power analysis using the G* Power software. Journal of Educational Evaluation for Health Professions, 18, 17.
Stata Statistical Software: Release 17. StataCorp LP; 2021.
IBM SPSS Statistics for Windows. Version 28. IBM Corp; 2021.
SAS/STAT(R) Version 9.22. SAS Institute, Inc.; 2016.
R: A language and environment for statistical computing. R Foundation for Statistical Computing; 2017. https://www.R-project.org/.
Centers for Disease Control and Prevention. End of the Federal COVID-19 Public Health Emergency (PHE) Declaration. Updated May 5, 2023. Retrieved July 19, 2023, https://www.cdc.gov/coronavirus/2019-ncov/your-health/end-of-phe.html
Dobransky, K., & Hargittai, E. (2021). Piercing the pandemic social bubble: Disability and social media use about COVID-19. American Behavioral Scientist, 65(12), 1698–1720.
Azhari, A., Toms, Z., Pavlopoulou, G., Esposito, G., & Dimitriou, D. (2022). Social media use in female adolescents: Associations with anxiety, loneliness, and sleep disturbances. Acta Psychologica, 229, 103706.
Barthorpe, A., Winstone, L., Mars, B., & Moran, P. (2020). Is social media screen time really associated with poor adolescent mental health? A time use diary study. Journal of Affective Disorders, 274, 864–870.
Kuhlman, K. R., Straka, K., Mousavi, Z., Tran, M.-L., & Rodgers, E. (2021). Predictors of adolescent resilience during the COVID-19 pandemic: Cognitive reappraisal and humor. Journal of Adolescent Health, 69(5), 729–736.
Árbol, J. R., Ruiz-Osta, A., & Montoro Aguilar, C. I. (2021). Personality traits, cognitive styles, coping strategies, and psychological impact of the COVID-19 pandemic lockdown on healthy youngsters. Behavioral Sciences, 12(1), 5.
Yang, X., Song, B., & Wu, A. (2021). Social, cognitive, and eHealth mechanisms of COVID-19–related lockdown and mandatory quarantine that potentially affect the mental health of pregnant women in China: Cross-sectional Survey Study. Journal of Medical Internet Research, 23(1), e24495.
Xu, C., Xu, Y., & Xu, S. (2020). Cognitive reappraisal and the association between perceived stress and anxiety symptoms in COVID-19 isolated people. Frontiers in Psychiatry, 11, 858.
Muñoz-Navarro, R., Malonda, E., Llorca-Mestre, A., Cano-Vindel, A., & Fernández-Berrocal, P. (2021). Worry about COVID-19 contagion and general anxiety: Moderation and mediation effects of cognitive emotion regulation. Journal of Psychiatric Research, 137, 311–318.
Mohr, D. C., & Goodkin, D. E. (1999). Treatment of depression in multiple sclerosis: Review and meta-analysis. Clinical Psychology: Science and Practice, 6(1), 1.
Cooney, G. M., Dwan, K., & Greig, C. A. (2013). Exercise for depression. Cochrane Database of Systematic Reviews. https://doi.org/10.1002/14651858.CD004366.pub6
Lopresti, A. L., Hood, S. D., & Drummond, P. D. (2013). A review of lifestyle factors that contribute to important pathways associated with major depression: Diet, sleep and exercise. Journal of Affective Disorders, 148(1), 12–27.
Schwartz, C. E., Zhang, J., Michael, W., Eton, D. T., & Rapkin, B. D. (2018). Reserve-building activities attenuate treatment burden in chronic illness: The mediating role of appraisal and social support. Health Psychology Open. https://doi.org/10.1177/2055102918773440
Hale, T., et al. Variation in government responses to COVID-19. Blavatnik School of Government, University of Oxford; 2020. Sep 1. https://www.bsg.ox.ac.uk/research/publications/variation-government-responses-covid-19
Pašović, M., Leach-Kemon, K., Troeger, C., Vos, T., Lozano, R. Countries Hit Hardest by COVID-19. Updated November 17. https://www.thinkglobalhealth.org/article/countries-hit-hardest-covid-19
Acknowledgements
We are grateful to Wesley Michael, M.B.A., of Rare Patient Voice, LLC, and IPSOS-Insight, LLC, for facilitating access to participants; and to the participants themselves who provided data for this project.
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CES and BDR designed the research study. CES and KB analyzed the data. CES wrote the paper, and KB and BDS edited the manuscript. All authors read and approved the final manuscript.
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Schwartz, C.E., Borowiec, K., Li, Y. et al. Individual differences in the long-term impact of the pandemic: moderators of COVID-related hardship, worry, and social support. Qual Life Res 33, 927–939 (2024). https://doi.org/10.1007/s11136-023-03573-0
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DOI: https://doi.org/10.1007/s11136-023-03573-0