Work Description

Title: Subjective Effect Reports of Food Open Access Deposited

h
Attribute Value
Methodology
  • This was a cross-sectional study in which 507 participants (n = 501 used in analyses) were recruited through Amazon MTurk to answer a survey about eating behavior. The survey was conducted using Qualtrics. In the online survey, participants were shown pictures of various food items, one at a time and in a randomized order, and asked to answer several questions about each food. Participants were instructed to respond to the questions while thinking about how they typically feel when they consume the pictured food. Cluster analytic techniques in SPSS Version 24.0 were utilized to explore how food items group together based on facets of subjective experience.
Description
  • The data set supports a study investigating which foods may be most implicated in addictive-like eating by examining how nutritionally diverse foods relate to loss of control consumption and various subjective effect reports. Participants (n = 501) self-reported how likely they were to experience a loss of control over their consumption of 30 nutritionally diverse foods and rated each food on five subjective effect report questions that assess the abuse liability of substances (liking, pleasure, craving, averseness, intensity). Hierarchical cluster analytic techniques were used to examine how foods grouped together based on each question. Highly processed foods, with added fats and/or refined carbohydrates, clustered together and were associated with greater loss of control, liking, pleasure, and craving. The clusters yielded from the subjective effect reports assessing liking, pleasure, and craving were most similar to clusters formed based on loss of control over consumption, whereas the clusters yielded from averseness and intensity did not meaningfully differentiate food items. The associated study applies methodology used to assess the abuse liability of substances to understand whether foods may vary in their potential to be associated with addictive-like consumption. Highly processed foods (e.g., pizza, chocolate) appear to be most related to an indicator of addictive-like eating (loss of control) and several subjective effect reports (liking, pleasure, craving). Thus, these foods may be particularly reinforcing and capable of triggering an addictive-like response in some individuals. Future research is warranted to understand whether highly processed foods are related to these indicators of abuse liability at a similar magnitude as addictive substances. The data set is presented in both .sav format for use with SPSS software and in csv format.
Creator
Depositor
  • eorenste@umich.edu
Contact information
Discipline
Keyword
Resource type
Last modified
  • 10/04/2019
Published
  • 07/20/2017
DOI
  • https://doi.org/10.7302/Z2GQ6VZJ
License
To Cite this Work:
Schulte, E. M. (2017). Subjective Effect Reports of Food [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/Z2GQ6VZJ

Relationships

This work is not a member of any user collections.

Files (Count: 3; Size: 554 KB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
ReadMe_ClusterAnalysisDeepBlue.pdf 2017-10-20 2017-10-20 83.9 KB Open Access
ClusterAnalysis_DeepBlue.sav 2017-07-20 2017-07-20 161 KB Open Access
ClusterAnalysis_DeepBlue.csv 2017-10-20 2017-10-20 309 KB Open Access

Download All Files (To download individual files, select them in the “Files” panel above)

Best for data sets < 3 GB. Downloads all files plus metadata into a zip file.

Files are ready   Download Data from Globus
Best for data sets > 3 GB. Globus is the platform Deep Blue Data uses to make large data sets available.   More about Globus

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.