Using emotional flow in patient testimonials to debias affective forecasting in health decision-making

ABSTRACT

People often overestimate the duration and intensity of emotional impact for future health events, leading to sub-optimal decision-making. Previous attempts to mitigate these affective forecasting errors have had only limited success. Across two experiments with different emotional appeals (fear and disgust), health contexts (genetic testing and colonoscopy), and samples (women and African American men), this research explored the potential of emotional flow in testimonials to attenuate affective forecasting errors and increase positive health outcomes. Both studies demonstrated that a narrative intervention that mirrored shifts in emotional intensity throughout health-screening procedure testimonials increased affective forecasting accuracy and behavioral intent. These findings highlight the importance of incorporating emotional shifts in narratives to facilitate individuals’ ability to better predict future emotional reactions.

KEYWORDS:

• Affective forecasting
• decision-making
• emotional flow
• genetic testing
• colonoscopy
• narrative persuasion

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The specific instructions were: “Please read the following statements carefully and indicate whether each statement appears in the text you have read (please mark all that apply). Note: This question does not ask whether the statements are true or false. Some statements may be true but did not appear in the text. Please make sure you only mark statements that appeared in the text.”

2 The attention check included four true/false questions, such as “the main character in the story had a family history of cancer” and “the main character in the story did not share her test results with family members.” Participants were excluded if they responded to two or more questions incorrectly.

3 A priori power analysis (G* Power 3; Faul et al., 2007) suggested that in order to detect a small-medium effect size of f  = .20 at α  = .05 and 1 –β = .80, the minimum required total sample size is 350.

Additional information

Funding

This work was supported by Delaney Family Foundation.