An integrated framework for predicting the consumption frequency of alcohol, hot beverages, sugary drinks, and water. (original) (raw)

Werner, Johanna, Papies, Esther K. ORCID logoORCID: https://orcid.org/0000-0002-8460-675X, Best, Maisy, Scheepers, Christoph ORCID logoORCID: https://orcid.org/0000-0002-3127-8764 and Barsalou, Lawrence ORCID logoORCID: https://orcid.org/0000-0002-1232-3152(2022) An integrated framework for predicting the consumption frequency of alcohol, hot beverages, sugary drinks, and water.Appetite, 169, 105546. (doi: 10.1016/j.appet.2021.105546)

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Abstract

Consumption of alcohol, sugary drinks, hot drinks, and water have typically been studied independently. Here we addressed them together by developing a model to predict consumption frequency (CF) across all of them in a single study. We used the Situated Assessment method to do so, first sampling drinks to cover a broad range of drinking situations, and then identifying potential predictors of CF from the situated action cycle (e.g., taste, healthiness, identity). 900 UK participants completed an online survey, rating 4 alcoholic and 7 non-alcoholic drinks on CF and on 34 predictors of drinking behaviour. A factor analysis found that the 34 predictors could be effectively simplified to 6 factors: habits, craving/regulation, negative consequences, health/functionality, positive taste, and socialising/positive consequences. We then used the six factor scores in mixed effects models to predict CF for all drinks combined, as well as for each drink individually. Our six-factor model explained 72% of the variance in CF at the group level and a median 66% at the individual level. Consistent with our hypotheses, habit, craving/regulation, and positive taste dominated prediction of CF for all drinks. Different patterns of prediction, however, resulted from healthiness/functionality, negative consequences, and socialising/positive consequences moderating the prediction CF, especially for alcoholic drinks and water. Our study illustrates a novel approach for predicting CF across drinks.

Item Type: Articles
Status: Published
Refereed: Yes
Glasgow Author(s) Enlighten ID: Scheepers, Dr Christoph and Papies, Dr Esther and Barsalou, Professor Lawrence and Best, Dr Maisy and Werner, Johanna
Authors: Werner, J., Papies, E. K., Best, M., Scheepers, C., and Barsalou, L.
College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name: Appetite
Publisher: Elsevier
ISSN: 0195-6663
ISSN (Online): 1095-8304
Published Online: 10 September 2021

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Deposit and Record Details

ID Code: 259955
Depositing User: Miss Leigh Bunton
Datestamp: 02 Dec 2021 16:32
Last Modified: 26 May 2022 14:40
Date of first online publication: 10 September 2021
Data Availability Statement: No