Abstract: The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world. Protocol registration: The stage 1 protocol for this Registered Report was accepted in principle on 12 May 2020. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.c.4878591.v1
|Number of pages||22|
|Journal||Nature Human Behaviour|
|Early online date||2 Aug 2021|
|Publication status||Published - 31 Aug 2021|
Bibliographical noteFunding Information:
This project was supported by funds from: the Amazon Web Services (AWS) Imagine Grant (to E.M.B.); the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (JSPS KAKENHI; 16h03079, 17h00875, 18k12015, and 20h04581 to Y.Y.); the research programme Dipartimenti di Eccellenza from the Ministry of Education University and Research (MIUR to N. Cellini and G.M. and the Department of General Psychology of the University of Padua); statutory funds of the University of Wroclaw (to A. Sorokowska); the Charles University Research Programme PROGRES (Q18 to M. Vranka); the Knut and Alice Wallenberg Foundation (2016:0229 to J.K.O.); the Rubicon Grant (019.183sg.007 to K.v.S.) from the Netherlands Organisation for Scientific Research; the Australian Research Council (dp180102384 to R.M.R.); the US National Institutes of Health (NIMH111640 to M.N.-D.), the Huo Family Foundation to N.J.; the NSF Directorate for Social, Behavioral and Economic Sciences, Division of Social and Economic Sciences (1559511 to J.S.L.); the US National Institutes of Health (RO1-CA-224545 to J.S.L.); Eesti Teadusagentuur–Estonian Research Council (PSG525 to A. Uusberg); the J. William Fulbright Program (to F. Azevedo); the HSE Basic Research Program (to D. Dubrov); Dominican University (a Faculty Development Grant to A. Krafnick); and the French National Research Agency Investissements d’avenir supporting PSF (ANR-15-IDEX-02 to H.I.); the Slovak Research and Development Agency (project no. APVV-20-0319 to M. Adamkovič); the programme FUTURE LEADER of Lorraine Université d’Excellence within the French National Research Agency Investissements d’avenir (ANR-15-IDEX-04-LUE to S.M.). Computation for this research was assisted by: the Harvard Business School compute cluster (HBSGrid); and the Open Science Grid. The Open Science Grid is supported by the National Science Foundation award 1148698 and the US Department of Energy’s Office of Science, as well as by the compute resources and assistance of the UW-Madison Center For High Throughput Computing (CHTC) in the Department of Computer Sciences. The CHTC is supported by UW-Madison, the Advanced Computing Initiative, the Wisconsin Alumni Research Foundation, the Wisconsin Institutes for Discovery, and the National Science Foundation, and is an active member of the Open Science Grid, which is supported by the National Science Foundation and the U.S. Department of Energy’s Office of Science. We thank data science specialist S. Worthington and the research computing environment at the Institute for Quantitative Social Science, Harvard University and V. Ivanchuk for research assistantship. Our semi-representative panels were made possible by an in-kind purchase from the Leibniz Institute for Psychology (protocol https://doi.org/10.23668/psycharchives.3012); a special grant from the Association for Psychological Science and a fee waiver from Prolific. This work was supported by a grant from the American Psychological Society (granted to the PSA). Further financial support was provided by the PSA and a special crowdfunding campaign initiated by the PSA. We thank Amazon Web Services for help with server needs, the Leibniz Institute for Psychology (ZPID) for help with data collection via the organization and implementation of semi-representative panels, Prolific Inc. for offering discounted recruitment, and Harvard University’s Institute for Quantitative Social Sciences for statistical consulting. Finally, this research was supported by resources from the Open Science Grid, which is supported by National Science Foundation award 1148698, and the U.S. Department of Energy’s Office of Science. Beyond those roles already acknowledged, the funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
© 2021, The Author(s), under exclusive licence to Springer Nature Limited.