Related Experiment Video
Updated: Feb 16, 2026

A Treatment Package without Escape Extinction to Address Food Selectivity
Published on: August 21, 2015
Using the Elaboration Likelihood Model to Address Drunkorexia among College Students
Tavis Glassman1, Peter Paprzycki2, Thomas Castor1
1a School of Population Health, University of Toledo , Toledo, Ohio , USA.
Peripheral prevention messages effectively reduced college student alcohol consumption and drunkorexia behaviors. Short, succinct messages are more impactful than complex ones for preventing harmful drinking patterns.
Area of Science:
- Psychology
- Public Health
- Behavioral Science
Background:
- College student alcohol consumption has severe consequences.
- Drunkorexia involves restricting calories or exercising to offset drinking calories.
- This study explores drunkorexia prevention strategies.
Purpose of the Study:
- To compare central and peripheral prevention messages using the Elaboration Likelihood Model.
- To assess the impact of these messages on alcohol consumption and drunkorexia.
- To identify effective communication strategies for college student behavior change.
Main Methods:
- Quasi-experimental design with 172 college students.
- Data collected via pre- or post-test measures.
- Weekly delivery of prevention messages (in-person, email, text).
Main Results:
- Peripheral messages significantly decreased alcohol consumption frequency.
- Participants exposed to peripheral messages reduced the number of drinks consumed.
- Peripheral messages also lowered the frequency of excessive drinking (more than five drinks).
Conclusions:
- Peripheral (short, succinct) messages are more effective than central (detailed) messages for preventing drunkorexia.
- Findings suggest designing brief messages for public health interventions.
- Further research is needed, but current results guide practical application.
Related Concept Videos
Elaborative Rehearsals
The effectiveness of...
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Student t Distribution
The Student t distribution was developed by William S. Goset (1876–1937) of the...
Microsoft Excel: Student's t-Test
To conduct a t-test in Excel, use the T.TEST function or the "Data...
Comparing Experimental Results: Student's t-Test
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...

