Evidence map›Paper›PMID 29538630›Full record

SynthesisAnnals of behavioral medicine : a publication of the Society of Behavioral Medicine2018

Interventions to Engage Affective Forecasting in Health-Related Decision Making: A Meta-Analysis.

Erin M Ellis, Glyn Elwyn, Wendy L Nelson, Peter Scalia, Sarah C Kobrin, Rebecca A Ferrer

Abstract readMeta-Analysis
In one paragraph

Synthesis in Annals of behavioral medicine : a publication of the Society of Behavioral Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
  2. IsAI & society · 2026
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  6. Activation Versus Change as a Principle Underlying Intervention Strategies to Promote Health Behaviors.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2023
    Review
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  16. Impact of Timing on Measurement of Decision Quality and Shared Decision Making: Longitudinal Cohort Study of Breast Cancer Patients.Medical decision making : an international journal of the Society for Medical Decision Making · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Erin M EllisBasic Biobehavioral and Psychological Sciences Branch, Behavioral Research Program, National Cancer Institute, Rockville, MD, USA.
Glyn ElwynThe Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, Lebanon, NH, USA.
Wendy L NelsonBasic Biobehavioral and Psychological Sciences Branch, Behavioral Research Program, National Cancer Institute, Rockville, MD, USA.
Peter ScaliaThe Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, Lebanon, NH, USA.
Sarah C KobrinBasic Biobehavioral and Psychological Sciences Branch, Behavioral Research Program, National Cancer Institute, Rockville, MD, USA.
Rebecca A FerrerBasic Biobehavioral and Psychological Sciences Branch, Behavioral Research Program, National Cancer Institute, Rockville, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ForecastingAnticipation, PsychologicalDecision MakingEmotionsHealth BehaviorHealth EducationHumans

Identifiers

PMID29538630
PMCPMC7189982

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.