Imagining a Personalized Scenario Selectively Increases Perceived Risk of Viral Transmission for Older Adults

Abstract

The COVID-19 pandemic has created a serious and prolonged public-health emergency. Older adults have been at substantially greater risk of hospitalization, ICU admission, and death due to COVID-19; as of February 2021, over 81% of COVID-19-related deaths in the U.S. occurred for people over the age of 65^1,2. 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 outcomes^3,4. Therefore, effectively communicating health-related risk information requires tailoring interventions to older adults' needs⁵. Using a novel informational intervention with a nationally-representative sample of 546 U.S. 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 they suggest new strategies to combat annual influenza outbreaks.

Keywords: COVID-19; aging; cognition; decision-making; episodic simulation; memory; risk perception; socioemotional selectivity.

Figure 1.

Prediction error drove change in perceived risk, but not for older adults. A) During Session 1 (immediately post-intervention), average information prediction error scores were positively associated with change in perceived risk for Younger Adults (Pearson’s r(232) = 0.24, 95% CI [0.11, 0.36], p = .0002) and Middle-Aged Adults (r(185) = 0.24, 95% CI [0.11, 0.38], p = .0007), but not Older Adults (Older Adults: r(113) = 0.16, 95% CI [−0.02, 0.33], p = .086). B) Model-derived slope estimates (standardized variables) corresponding to the raw data depicted in panel A, indicating the main effect of prediction error after controlling for the effect of intervention condition. The interaction between prediction error and age (continuous) was non-significant, β = −0.04, 95% CI [−0.13, 0.05], t = −0.95, p = .343. C) During Session 2 (1–3 weeks post-intervention), prediction error was positively associated with change in perceived risk for Younger Adults (r(202) = 0.29, 95% CI [0.16, 0.41], p = .00002) and Middle-Aged Adults (r(172) = 0.19, 95% CI [0.04, 0.33], p = .012), but not Older Adults (Older Adults: r(108) = −0.02, 95% CI [−0.21, 0.16], p = .806). D) Model-derived slope estimates (standardized variables) corresponding to the raw data depicted in panel C, indicating the main effect of prediction error after controlling for intervention condition and delay duration. There was an interaction between prediction error and age (continuous), β = −0.15, 95% CI [−0.24, −0.06], t = −3.30, p = .001. Overall Notes: Points in panels A and C depict subject scores (jittered for visualization), and lines depict correlations for each age group (two-sided tests, not corrected for multiple comparisons). Lines in panels B and D depict slopes derived from multiple linear regression models that included age as a continuous variable (two-sided omnibus tests). Error bands indicate 95% confidence intervals.

Figure 2.

Comparing the effects of episodic simulations (Personal, Impersonal, and Unrelated) on perceived risk across the adult lifespan. A) During Session 1 (immediately post-intervention), there were no statistically significant associations between age and change in perceived risk (Personal: r(179) = 0.10, 95% CI [−0.05, 0.24], p = .175; Impersonal: r(178) = 0.03, 95% CI [−0.12, 0.17], p = .739; Unrelated: r(182) = 0.004, 95% CI [−0.14, 0.15], p = .953). B) Model-derived slope estimates (standardized variables) corresponding to the raw data depicted in panel A, indicating the main effect of simulation condition after controlling for prediction error. The interaction between simulation condition and age (continuous) was non-significant (F_(2,524) = 0.79, p = .455). C) During Session 2 (1–3 weeks post-intervention), there was a positive association between age and change in perceived risk, selectively in the Personal simulation condition (Personal: r(156) = 0.16, 95% CI [0.003, 0.31], p = .045; Impersonal: r(163) = −0.03, 95% CI [−0.18, 0.13], p = .731; Unrelated: r(169) = −0.04, 95% CI [−0.19, 0.11], p = .608). D) Model-derived slope estimates (standardized variables) corresponding to the raw data depicted in panel C, indicating the main effect of simulation condition after controlling for prediction error and delay duration. There was an interaction between simulation condition and age (continuous) (F_(2,475) = 3.41, p = .034). Overall Notes: Points in panels A and C depict subject scores (jittered for visualization), and lines depict correlations for each condition (two-sided tests, not corrected for multiple comparisons). Lines in panels B and D depict slopes derived from multiple linear regression models (two-sided omnibus tests). Error bands indicate 95% confidence intervals.

Figure 3.

Testing the effects of age and episodic simulation (Personal, Impersonal, and Unrelated) on change in information seeking about local COVID-19 risk. A) Older adults in the Personal simulation condition reported increases in independent information-seeking about local risk statistics during the post-intervention delay period (Personal: r(156) = 0.22, 95% CI [0.07, 0.36], p = .006; Impersonal: r(163) = −0.11, 95% CI [−0.26, 0.04], p = .149; Unrelated: r(169) = −0.06, 95% CI [−0.21, 0.09], p = .441). B) Model-derived slope estimates (standardized variables) corresponding to the raw data depicted in panel A, depicting the effect of age on change in information-seeking after controlling for prediction error and delay duration. There was an interaction between simulation condition and age (continuous) (F_(2,475) = 5.92, p = .003). Overall Notes: Points depict subject scores (jittered for visualization). Lines depict correlations for each condition (two-sided tests, not corrected for multiple comparisons). Error bands indicate 95% confidence intervals.