Abstract
The COVID-19 pandemic has created a serious and prolonged public health emergency. Older adults have been at substantially greater risk of hospitalization, intensive care unit admission and death due to COVID-19. As of February 2021, over 81% of COVID-19-related deaths in the US occurred in people over the age of 65 (refs. 1,2). Converging evidence from around the world suggests that age is the greatest risk factor for severe COVID-19 illness and for the experience of adverse health outcomes3,4. Therefore, effectively communicating health-related risk information requires tailoring interventions to the needs of older adults5. Using a new informational intervention with a nationally representative sample of 546 US residents, we found that older adults reported increased perceived risk of COVID-19 transmission after imagining a personalized scenario with social consequences. Although older adults tended to forget numerical information over time, the personalized simulations elicited increases in perceived risk that persisted over a 1–3 week delay. Overall, our results bear broad implications for communicating information about health risks to older adults and suggest new strategies to combat annual influenza outbreaks.
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News and social media have repeatedly documented the risky behaviors of Americans throughout the pandemic and recent survey evidence suggests that Americans tend to underestimate the risk related to COVID-19 transmission6. As COVID-19 has spread, so too has misinformation about both the efficacy of different preventative behaviors (for example, mask-wearing, hand-washing) and the risks of engaging in certain commonplace behaviors where the virus could be transmitted (for example, grocery shop**, indoor dining, air travel). Unfortunately, those most at risk of severe illness and death due to COVID-19 (that is, older adults) are also most susceptible to believing misinformation. Older adults are far more likely to believe and share false information from social media7,8,9 and this problem is getting worse as increasing numbers of older adults become active on social media10.
To combat COVID-19-related misinformation and ensure that individuals who are most at risk for severe illness (older adults) possess the information needed to make informed decisions, it is critical to develop interventions that meet the needs of older adults by (1) effectively conveying the risks of engaging in behaviors that could cause viral transmission and (2) ensuring that risk information sticks over time. We developed an interactive intervention that would inform individuals about COVID-19-related risks, with the intention of improving downstream compliance with public health measures6. In the present study, we tested the efficacy of our intervention across the adult life span and compared strategies for communicating risk information to older adults. Drawing on theoretical frameworks of aging and motivation11,12, we designed our intervention to include elements that could optimize learning for older adults.
Past efforts to develop interventions for improving risk estimation have shown some success but the effect sizes across interventions are typically small; also, effects rapidly diminish over relatively short delays13,14,15,16. Although older adults typically self-report being more risk-averse17, their choice behavior is not always consistent with their stated preferences18. In some situations, older adults take more risks than younger adults19. Furthermore, older adults tend to seek out less information about risk12, which can have negative consequences for their health-related decisions20,21. Older adults are more prone to deliberately choose ignorance, especially when new information could be negative22. These problems may also be exacerbated because older adults tend to be less successful at learning from numerical feedback23,24.
However, personalized social information may help motivate older adults to improve risk literacy. Socioemotional selectivity theory (SST) posits that older adults are more motivated to make decisions that maximize emotional meaning, enhance social connections and emphasize personally relevant factors11,25,26. Prioritizing personally relevant social connections is adaptive when one perceives limited time left in life; bolstering social connections can offer emotional rewards and the practical benefits of a support network11,27. Importantly, these motivational changes that occur later in life correspond to broad changes in decision-making, emotion regulation, learning and information-seeking11,12.
Leveraging these theoretical insights from SST, we predicted that if older adults are more motivated to attend to personally relevant social information, then they may be more responsive to an intervention that involves generating rich, personalized mental imagery about close others. Past studies have used a type of mental imagery, termed episodic simulation, to enhance subsequent decision-making processesMethods). Next, we randomly assigned participants to complete one of three variants of the episodic simulation task (Methods). In the personal simulation condition, participants imagined a scenario where they hosted a dinner party attended by four specific close others (for example, friends, neighbors). In this scenario, a guest became seriously ill with COVID-19, exposed the other guests to the disease and also infected the host. In the impersonal simulation condition, participants imagined a fictional character experiencing the same scenario. In the unrelated (control) condition, participants imagined a scenario that was neither personalized nor related to COVID-19. This control condition equated attention and time on task but we did not expect this unrelated imagination exercise to influence subsequent learning. The episodic simulation was always the first part of the intervention because previous studies showed that an imagination exercise influences subsequent decision-making37,38. Crucially, older adults reported greater long-lasting increases in perceived risk only when they imagined the possible outcomes of risky decisions that affected themselves and close others. Imagining an impersonal or unrelated scenario did not increase perceived risk in older adults, either immediately or after a delay.
In an additional exploratory analysis, we also found that for older adults only, the personalized episodic simulation was associated with increased information-seeking. During the post-intervention delay period (1–3 weeks), older adults (but not younger adults) who received the personalized simulation reported actively consuming more information about local COVID-19 risk levels relative to their pre-intervention habits. This finding suggests that the personalized episodic simulation helped motivate ongoing learning and cultivate a habit of information-seeking. Recent research has shown that older adults tend to be less willing to seek new information, even deliberately choosing ignorance when the information could be negative22. Our intervention offers a promising new method to encourage information-seeking in older adults. Overall, our results suggest that including a personalized imagination exercise can enhance the efficacy of interventions that target older adults, facilitating longer-term learning and better health-related decision-making.
We found that the effect of numerical risk information on older adults was weakened over time but the personalized imagination exercise elicited lasting increases in perceived risk and information-seeking. Older adults may be more prone to forgetting numerical risk information. However, another possibility is that they could have replaced or updated this knowledge with new information that was encountered after the intervention. We tested this account by comparing risk estimation accuracy during session 2 but did not find evidence that older adults who engaged in more information-seeking became more accurate at estimating updated risk levels, regardless of the intervention condition (Supplementary Information, Session 2 risk estimation accuracy). Overall, our results support the idea that older adults are more likely to forget numerical risk information but personalized elements can elicit long-term intervention effects.
Taken together, our results suggest that certain strategies are more effective for inducing longer-term increases in perceived risk for older adults. Although older adults may be more prone to forgetting numerical information, a personalized episodic simulation may enhance both learning retention and information-seeking behaviors over time. Overall, both of these mechanisms may contribute to the beneficial effects of our intervention. Our results are generally consistent with the fundamental tenets of SST, which posits that older adults are more motivated to reinforce social connections and seek information that is personally relevant or emotionally meaningful11,12,25. Imagining a personalized scenario that connects information with existing semantic and episodic memories may be an effective way to make risk information more memorable for older adults. Personalized interventions situate risk information in context, drawing on social connections to enhance salience. Our results also align with previous studies on episodic simulation, which have shown that imagining future scenarios can influence decision-makingEpisodic simulation task The episodic simulation task involved guided imagination through one of three scenarios that illustrated the potential consequences of risky decisions. During the simulation, participants were instructed to visualize events and details, then type responses in a text box. Participants were randomly assigned to one of three episodic simulation conditions in a between-subject design: the personal simulation (session 1: n = 181, session 2: n = 158); impersonal simulation (session 1: n = 180, session 2: n = 165); or unrelated simulation (session 1: n = 184, session 2: n = 171). In the personal simulation, participants imagined themselves hosting a dinner party in their home with four specific close others (for example, friends or neighbors) as guests. Participants identified each guest by first name and/or relationship (for example, ‘my sister Maria’), then visualized the guests and setting (for example, the dining room) in as much detail as possible. In this scenario, a guest began exhibiting symptoms of COVID-19 during dinner. The guest later confirmed a diagnosis and was hospitalized. The host then informed the other dinner party guests of the exposure and eventually also became ill with COVID-19. The impersonal simulation depicted a fictional character and his friends undergoing the same scenario. The unrelated simulation described a scenario that was thematically related (a story about rabbits falling ill after eating rotten vegetables) but did not include any personalized or COVID-related elements. The full text for all simulation conditions is provided in the Supplementary Information (Episodic simulation text). After the episodic simulation, participants completed the risk estimation task, which involved estimating numerical risk levels in their local community. Participants received a brief tutorial about risk and probability, then were instructed to think about events of seven different sizes (5, 10, 25, 50, 100, 250 and 500 people) that could happen in their location. For each event size, participants estimated the probability (0% = impossible ... 100% = definitely) that at least one of the people attending the event was infected with COVID-19. After estimating the risk levels for all event sizes, participants received veridical feedback about actual risk probabilities. Actual risk values were calculated based on the prevalence of active COVID-19 cases in each participant’s county of residence41. We calculated the information prediction error as a measure of misestimation, the average discrepancy between estimated and actual risk values across event sizes6. Statistical analyses were conducted using multiple linear regression. Continuous variables were standardized before submission to multiple linear regression. Factor variables for conditions were effect-coded. Visual inspection of histograms indicated that several variables exhibited high kurtosis, with some extreme values at both tails of the distribution. As a result, residuals from fitted models were larger for values at the tails. To correct for high kurtosis and meet the assumption of normality, we winsorized extreme values to the 5th and 95th percentiles. The variable for change in perceived risk (session 1) was winsorized. As reported in detail elsewhere, winsorization improved model fits but did not change the statistical significance of our findings6. Additionally, we log-transformed the variable for actual risk (that is, local case prevalence) to account for skewing. Other variables were not transformed because distributions were approximately normal. Figures were produced using the ggplot2 (ref. 42) v.3.3.2 and sjPlot43 v.2.8.6 packages. Further information on research design is available in the Nature Research Reporting Summary linked to this article.Risk estimation task
Statistics
Reporting Summary
Data availability
Raw and cleaned data are provided online via the Open Science Framework (https://osf.io/35us2/)44. Open-ended written responses to the episodic simulation have been omitted from the raw data to protect participant privacy and because personalized scenarios may include identifiable data. The full set of written responses from the episodic simulation task can be provided upon reasonable request, with institutional review board approval.
Code availability
Statistical analyses were conducted using multiple linear regression in R v.4.0.3, implemented with RStudio v.1.3.1093. All scripts are provided online via the Open Science Framework44. These scripts reproduce all data cleaning procedures, analyses and plots used to generate the results reported in the manuscript.
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Acknowledgements
The study was funded by discretionary funding from the Duke Trinity College of Arts and Sciences to G.R.S.-L. and a US National Institute on Aging grant (no. R01-AG058574) awarded to G.R.S.-L. and R.C. A.H.S. is supported by a Graduate Research Fellowship from the National Science Foundation and a Postgraduate Scholarship from the Natural Sciences and Engineering Research Council of Canada. We thank A. Chande, S. Lee, Q. Nguyen, S. J. Beckett, T. Hilley, C. Andris and J. S. Weitz at Georgia Tech and M. Harris at Stanford for openly sharing the tools they developed to assess local virus levels, which made the present studies possible.
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A.H.S., S.H., M.L.S. and G.R.S.-L. designed the studies. A.H.S., S.H. and M.L.S. created the stimuli and survey materials. A.H.S. performed the data collection. A.H.S. analyzed the data with input from S.H., M.L.S., R.A.A., R.C. and G.R.S.-L. A.H.S. and M.L.S. drafted the paper, with input from S.H., R.A.A., R.C. and G.R.S.-L. All authors approved the final version.
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Supplementary Information
Episodic Simulation Text, Table 1 and Session 2 Risk Estimation Accuracy.
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Sinclair, A.H., Stanley, M.L., Hakimi, S. et al. Imagining a personalized scenario selectively increases perceived risk of viral transmission for older adults. Nat Aging 1, 677–683 (2021). https://doi.org/10.1038/s43587-021-00095-7
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DOI: https://doi.org/10.1038/s43587-021-00095-7
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