A smartphone based attentive eating intervention for energy intake and weight loss: A randomised controlled trial 2017-2018

Whitelock, Victoria and Kersbergen, Inge and Higgs, Suzanne and Aveyard, Paul and Halford, Jason and Robinson, Eric (2018). A smartphone based attentive eating intervention for energy intake and weight loss: A randomised controlled trial 2017-2018. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-853371

Obesity is now a major biosocial issue that affects most of the developed world. Rises in obesity have been caused primarily by increases in the amount of food people have been eating. However, the long-term reductions to food consumption which are required to promote weight loss are difficult to achieve for most people on their own. Recent work has shown that memory for recent eating experiences is an important determinant of eating behaviour; by having an accurate memory representation of what we have been eating throughout the day, we can make better decisions about how much to eat. This raises the possibility of developing intervention tools that target memory for recent eating in order to help people eat more healthily. In line with this, initial results suggest that encouraging individuals to eat in a more 'attentive' manner, by ensuring attention is paid towards meals being eaten, improves memory for recent eating and reduces the amount of food people tend to eat. These finding are promising as even modest reductions to food consumption can promote weight loss and therefore have positive effects on health and well-being.
Although it has been suggested that memory informs food consumption, how this process occurs is unclear. If we are able to understand how 'attentive' eating reduces food consumption, this may have public health benefit. Thus, a thorough investigation of how memory influences eating behaviour is now required. Memory for recent eating consists of multiple episodic 'elements', such as visual memory for meal size and memory for how filling a meal is remembered to have been. Understanding which episodic 'elements' determine how much food we consume and explain why eating attentively reduces food consumption will provide us with novel theoretical information. This in turn will also enable us to design effective intervention tools to target memory for recent eating, so we can help overweight individuals eat less and lose weight, which will be of wider public health benefit.
This research will be the first to develop a theoretical account of how memory for recent eating influences eating behaviour and the process by which an 'attentive' eating style reduces food consumption. This new knowledge will then guide a translational 'real world' intervention study; taking the findings from initial promising laboratory studies and using cross-disciplinary methods to test whether 'attentive eating' principles can be applied in a public health intervention setting to help overweight people eat less and lose weight.
The present project will address an important biosocial question and benefit from cross-disciplinary research methods. By the end of the research we will have developed a better understanding of how memory for recent eating influences food consumption and examined the applied relevance of this new knowledge. These novel insights will have the potential to help tackle the widespread obesity problem faced by the majority of the developed world.

Data description (abstract)

The data in this collection are from a randomised controlled trial testing whether a smartphone application that encourages a more attentive eating style reduces energy intake and promotes weight loss.
Studies 1 and 2 use laboratory feeding methods to manipulate and interfere with memories for recent eating, in order to test and understand the influence that different memory elements have on food consumption. Study 3 tests whether targeting memory for recent eating and promoting a more 'attentive' approach to eating through the use of smartphone technology can help overweight and obese individuals eat less and lose weight.

Downloads

Altmetric