Effects of Physical Activity Calorie Equivalent Labelling on Hot Drink Vending Transactions

Launched by LOUGHBOROUGH UNIVERSITY · Oct 2, 2023

Keywords

ClinConnect Summary

This clinical trial is studying a new way to help people make healthier choices when buying drinks from vending machines. The researchers want to see if showing how much physical activity is needed to burn off the calories in hot beverages—like coffee or hot chocolate—can lead to healthier purchasing habits. For example, if a drink contains enough calories to require 30 minutes of walking to burn off, this information would be displayed on the vending machine. The goal is to see if this kind of labelling can reduce the average number of calories people buy from these machines.

To participate in the trial, individuals need to be in areas where the vending machines are self-service and operated by a participating university. The study will involve 42 vending machines over 27 locations, and participants can expect to see changes in labelling over 24 weeks. Initially, the machines will not have the new labels, but over time, some of the machines will start to display the physical activity information. Researchers will track how many drinks are bought and whether the new labels affect purchasing behavior. This trial aims to find better ways to encourage healthier choices in a simple and understandable way.

Gender

ALL

Eligibility criteria

• Inclusion Criteria:
• • All vending machines within a cluster need to be self-service customer operated.
• • Vending machines need to be operated and maintained by the participating University
• • All vending machines within a cluster need to dispense the same hot beverages of interest
• • All vending machines in the cluster electronically record transactions of the hot beverages.
• Exclusion Criteria:
• • Vending machines or clusters not meeting the inclusion criteria
• • Not able to access the vending machine for the purpose of this study
• • Any vending machine the ethics committee feel it is inappropriate to collect data from (e.g. near a medical facility), in which the case the whole cluster (location) will be excluded