Evidence map›Paper›PMID 36920917›Full record

ArticlePloS one2023

Compensatory eating after exercise in everyday life: Insights from daily diary studies.

Natalie M Reily, Rebecca T Pinkus, Lenny R Vartanian, Kate Faasse

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Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

4 authors.

Natalie M ReilyBlack Dog Institute, Randwick, NSW, Australia.
Rebecca T PinkusSchool of Psychology, The University of Sydney, Sydney, NSW, Australia.
Lenny R VartanianSchool of Psychology, UNSW Sydney, Sydney, NSW, Australia.ORCID 0000-0001-8238-6151
Kate FaasseSchool of Psychology, UNSW Sydney, Sydney, NSW, Australia.ORCID 0000-0003-3073-3718

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is considerable variability in how successful people are in losing weight via exercise programs. Experimental research suggests that greater food intake after exercise may be one factor underlying this variability, but no studies have assessed patterns of post-exercise eating behaviour over time in naturalistic settings. Thus, we aimed to assess how exercise and contextual factors (e.g., hunger, presence of others) influence the healthiness and amount of food eaten after exercise in two daily diary studies. In Study 1, participants (n = 48) reported their food intake and exercise daily for 28 days. For each meal, they provided a brief description of the food(s) eaten which were then categorised as healthy, unhealthy, or mixed (neither healthy nor unhealthy) by two independent coders. Study 2 used the same method, but participants (n = 55) also reported the portion size of each meal. Hierarchical linear modelling showed that in Study 1, contrary to expectations, post-exercise meals were less likely to be unhealthy (relative to mixed) than were random meals from non-exercise days (OR = 0.63, p = .011), and that participants ate proportionally fewer unhealthy meals on exercise days compared to non-exercise days (b = -4.27, p = .004). Study 2 replicated these findings, and also found that participants consumed larger meals after exercise in comparison to random meals from non-exercise days (b = 0.25, p < .001). Participants were not consistently engaging in compensatory eating by eating less healthily after exercise compared to on non-exercise days, but they did eat larger portions post-exercise. This work highlights the need for naturalistic methods of assessing compensatory eating, and has the potential to facilitate development of strategies to improve health behaviour regulation.

Indexed as

EatingFeeding BehaviorEnergy IntakeHumansHungerMealsTime Factors

Identifiers

PMID36920917
PMCPMC10016725

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Registered trials

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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.