Erin M Ellis1, Glyn Elwyn2, Wendy L Nelson1, Peter Scalia2, Sarah C Kobrin1, Rebecca A Ferrer1. 1. Basic Biobehavioral and Psychological Sciences Branch, Behavioral Research Program, National Cancer Institute, Rockville, MD, USA. 2. The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, Lebanon, NH, USA.
Abstract
Background: People often use affective forecasts, or predictions about how a decision will make them feel, to guide medical and health decision making. However, these forecasts are susceptible to biases and inaccuracies that can have consequential effects on decision making and health. Purpose: A meta-analysis was performed to determine the effectiveness of intervening to address affective forecasting as a means of helping patients make better health-related choices. Methods: We included between-subjects experimental and intervention studies that targeted variables related to affective forecasting (e.g., anticipated regret, anticipated affect) as a means of changing health behaviors or decisions. We determined the overall effect of these interventions on targeted affective constructs and behavioral outcomes, and whether conceptual and methodological factors moderated these effects. Results: A total of 133 independent effect sizes were identified from 37 publications (N = 72,020). Overall, affective forecasting interventions changed anticipated regret, d = 0.24, 95% confidence interval (CI) (0.15, 0.32), p < .001, behavior, d = 0.29, 95% CI (0.13, 0.45), p < .001, and behavioral intentions, d = 0.19, 95% CI (0.11, 0.28), p < .001, all measured immediately postintervention. Interventions did not change anticipated positive and negative affect, and effects on intentions and regret did not extend to follow-up time points, ps > .05. Generally, effects were not moderated by conceptual model, intervention intensity, or behavioral context. Conclusions: Affective forecasting interventions had a small consistent effect on behavioral outcomes regardless of intervention intensity and conceptual framework, suggesting such constructs are promising intervention targets across several health domains.
Background: People often use affective forecasts, or predictions about how a decision will make them feel, to guide medical and health decision making. However, these forecasts are susceptible to biases and inaccuracies that can have consequential effects on decision making and health. Purpose: A meta-analysis was performed to determine the effectiveness of intervening to address affective forecasting as a means of helping patients make better health-related choices. Methods: We included between-subjects experimental and intervention studies that targeted variables related to affective forecasting (e.g., anticipated regret, anticipated affect) as a means of changing health behaviors or decisions. We determined the overall effect of these interventions on targeted affective constructs and behavioral outcomes, and whether conceptual and methodological factors moderated these effects. Results: A total of 133 independent effect sizes were identified from 37 publications (N = 72,020). Overall, affective forecasting interventions changed anticipated regret, d = 0.24, 95% confidence interval (CI) (0.15, 0.32), p < .001, behavior, d = 0.29, 95% CI (0.13, 0.45), p < .001, and behavioral intentions, d = 0.19, 95% CI (0.11, 0.28), p < .001, all measured immediately postintervention. Interventions did not change anticipated positive and negative affect, and effects on intentions and regret did not extend to follow-up time points, ps > .05. Generally, effects were not moderated by conceptual model, intervention intensity, or behavioral context. Conclusions: Affective forecasting interventions had a small consistent effect on behavioral outcomes regardless of intervention intensity and conceptual framework, suggesting such constructs are promising intervention targets across several health domains.
Authors: Karen R Sepucha; Aisha T Langford; Jeffrey K Belkora; Yuchiao Chang; Beverly Moy; Ann H Partridge; Clara N Lee Journal: Med Decis Making Date: 2019-07-29 Impact factor: 2.583
Authors: Arielle S Gillman; Irina A Iles; William M P Klein; Barbara B Biesecker; Katie L Lewis; Leslie G Biesecker; Rebecca A Ferrer Journal: J Behav Med Date: 2021-09-04
Authors: Frank Doyle; Karen Morgan; Mary Mathew; Princy Palatty; Prashanti Kamat; Sally Doherty; Jody Quigley; Josh Henderson; Ronan O'Carroll Journal: Int J Environ Res Public Health Date: 2019-04-11 Impact factor: 3.390