Evidence map›Paper›PMID 32810166›Full record

ArticlePloS one2020

Translating episodic future thinking manipulations for clinical use: Development of a clinical control.

Jillian M Rung, Leonard H Epstein

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Trial
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  3. Review
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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

2 authors.

Jillian M RungDepartment of Pediatrics, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY, United States of America.ORCID 0000-0002-5945-6454
Leonard H EpsteinDepartment of Pediatrics, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY, United States of America.

Funding

UF Substance Abuse Training Center in Public HealthT32DA035167 · NIDA · UNIVERSITY OF FLORIDA · PI Linda B. Cottler · 2014 to 2026
$4.8M
Using prospection to improve obesity treatmentR01HD080292 · NICHD · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI EPSTEIN, LEONARD H · 2015 to 2019
$2.8M
NICHD NIH HHS R01 HD080292NIDA NIH HHS T32 DA035167
6 · The paper itself

Abstract

Many studies support that Episodic Future Thinking (EFT) reduces maladaptive health behaviors and how much individuals devalue the future (steepness of delay discounting). In order to understand the clinical utility of EFT, a control procedure that equates groups in non-specific treatment factors (e.g., expectancy of change, perceived connection of content to health behavior) is needed. The present research evaluated the effects of EFT relative to a novel control (health information thinking; HIT), which was designed to be structurally similar to EFT while incorporating elements from existing clinical controls. In a sample of Amazon Mechanical Turk workers (N = 254), we found that EFT reduced discounting relative to the HIT procedure and a standard EFT control. There were some affective differences across groups and differences in adherence to the intervention content, but these were unrelated to discounting. Delay discounting was not equivalent across the control groups, but this may not be a necessary condition to fulfill for a clinical control. Future research will need to identify whether the HIT procedure serves as a good control for other dependent variables when studying EFT and develop controls analogous to usual care or a "wait-list" in clinical contexts.

Indexed as

Delay DiscountingAdultArea Under CurveBody Mass IndexFemaleHealth BehaviorHumansMaleMemory, EpisodicROC CurveSurveys and Questionnaires

Identifiers

PMID32810166
PMCPMC7433876

What OpenQuestion holds

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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.