Main content

Contributors:

Date created: | Last Updated:

: DOI | ARK

Creating DOI. Please wait...

Create DOI

Category: Project

Description: Impulsive traits, broadly construed, are thought to play a role in under- and over-weight. For example, the behavioural economic factor of delay discounting, which describes preferences between smaller sooner and larger later rewards, is thought to be predictive of body composition. However, the evidence base is mixed, consisting of studies with varying methodological approaches and effect sizes. Two studies (N = 384 and 401) explored the potential relationships between discounting for money, weight loss, and food rewards to body mass index or waist-to-height ratio measures. Three proposals are considered: 1) that discounting and body composition are correlated, 2) that discounting influences body composition even after accounting for the confounder of age, and 3) that discounting moderates the rate of weight gain over time. Using Bayesian methods, we find evidence against all 3 proposals for discounting of multiple commodities and two different measures of body composition. We test, and dismiss, the possibility that lack of evidence is due to higher evidence thresholds in Bayesian than Frequentist methods. These findings and the varied results in the literature suggest that if discounting and body composition are associated, it is probably through complex indirect pathways of mediating variables.

Has supplemental materials for No simple link between temporal discounting and body composition on PsyArXiv

Files

Loading files...

Citation

Tags

Recent Activity

Loading logs...

OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
Accept
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.
Accept
×

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.