ArticleHealth psychology and behavioral medicine2025
What's in a cue?: Using natural language processing to quantify content characteristics of episodic future thinking in the context of overweight and obesity.
Article in Health psychology and behavioral medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Episodic future thinking (EFT), an intervention in which participants vividly imagine their future, has been explored as a cognitive intervention to reduce delay discounting and decrease engagement in harmful health behaviors. In these studies, participants generate text descriptions of personally meaningful future events. The content of these text descriptions, or cues, is heterogeneous and can vary along several dimensions (e.g. references to health, celebrations, family; vividness; emotional valence). However, little work has quantified this heterogeneity or potential importance for EFT's efficacy. To better understand the potential impact of EFT content in the context of health behavior change (e.g. diet) among people with or at risk for obesity and related conditions, we used data from 19 prior EFT studies, including 1705 participants (mean body mass index = 33.1) who generated 9714 cues. We used natural language processing to classify EFT content and examined whether EFT content moderated effects on delay discounting. Cues most commonly involved recreation, food, and spending time with family, and least commonly involved references to health and self-improvement. Cues were generally classified as highly vivid, episodic, and positively valent (consistent with the intervention's design). In multivariate regression with model selection, EFT content did not significantly moderate the effect of the episodic thinking intervention. Thus, we find no evidence that any of the content characteristics we examined were important moderators of the efficacy of EFT in reducing delay discounting. This suggests that EFT's efficacy is robust against variability in these characteristics. However, note that in all studies, EFT methods were designed to generate high levels of vividness, episodicity, and emotional valence, potentially resulting in a ceiling effect in these content areas. Moreover, EFT content was not experimentally manipulated, limiting causal inference. Future studies should experimentally examine these and other content characteristics and evaluate their possible role in EFT's efficacy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.