Evidence map›Paper›PMID 40469451›Full record

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.

Haylee Downey, Shuangshuang Xu, Sareh Ahmadi, Aditya Shah, Jeremiah M Brown, Warren K Bickel, Leonard H Epstein, Allison N Tegge, Edward A Fox, Jeffrey S Stein

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Haylee DowneyFralin Biomedical Research Institute at VTC, Roanoke, VA, USA.
Shuangshuang XuFralin Biomedical Research Institute at VTC, Roanoke, VA, USA.
Sareh AhmadiDepartment of Computer Science, Blacksburg, VA, USA.
Aditya ShahDepartment of Computer Science, Blacksburg, VA, USA.
Jeremiah M BrownFralin Biomedical Research Institute at VTC, Roanoke, VA, USA.
Warren K BickelFralin Biomedical Research Institute at VTC, Roanoke, VA, USA.
Leonard H EpsteinDepartment of Pediatrics, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA.
Allison N TeggeFralin Biomedical Research Institute at VTC, Roanoke, VA, USA.
Edward A FoxDepartment of Computer Science, Blacksburg, VA, USA.
Jeffrey S SteinFralin Biomedical Research Institute at VTC, Roanoke, VA, USA.

Funding

Episodic Future Thinking to Improve Management of Type 2 Diabetes in Rural and Urban Patients: Remote Delivery and Outcomes Assessment to Increase Reach and DisseminationR01DK129567 · NIDDK · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI STEIN, JEFFREY SCOTT · 2021 to 2023
$1.3M
NIDDK NIH HHS R01 DK129567
6 · The paper itself

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

Delay discountingepisodic future thinkingintervention effectivenessnatural language processingobesitytype 2 diabetes

Identifiers

PMID40469451
PMCPMC12135091

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.