Distraction, restrained eating and disinhibition: An experimental study of food intake and the impact of ‘eating on the go’

Abstract

To assess the impact of distraction on subsequent eating, 60 females consumed a cereal bar while watching TV, walking or talking, and their subsequent desire to eat and food intake were assessed. No effects were found for desire to eat. But while those higher in restrained eating consumed less overall and fewer calories after watching TV or talking, they consumed more overall and more calories (specifically five times more chocolate) if the cereal bar was eaten while walking. 'Eating on the go' may disinhibit restrained eaters either as a form of distraction or by offering a justification to overeat.

The Eating Motivation Survey: results from the USA, India and Germany

“…There is a large variety of different determinants and motives for why people eat what they eat, such as good taste, weight control or health reasons (7)(8)(9) . The first systematic attempt to assess the different motives for why people eat what they eat was in a scale developed by Steptoe et al (the Food Choice Questionnaire (FCQ) (8) ; see also Konttinen et al (10) ).…”

The impact of intuitive eating v. pinned eating on behavioural markers: a preliminary investigation

“…All items were measured on a Likert scale ranging from 'not at all' (1) to 'very much' (5). These items were based on those used in previous research to assess changes in the drive to eat in a number of different situations (8,10) .…”

“…In contrast to a focus on internal cues, an alternative strategy to weight management involves an emphasis on external cues, specifically time, known as PE. Research shows that an increasing number of people 'Eat on the go' and that there is a shift towards snacking rather than consuming meals (10)(11)(12) . Furthermore, this shift away from designated meals is associated with a reduction in vegetable intake, overeating and subsequent weight gain (5,6) .…”

Towards a Practical Pedestrian Distraction Detection Framework using Wearables

“…In addition to shortcomings such as reliance on smartphone camera feed or other specialized sensors and devices which limits their functionality, several of these schemes employ computationally-intensive data processing techniques that are challenging to implement on resource-constrained mobile and wearable devices. More importantly, the above techniques fail to generalize the problem of pedestrian distraction detection by not considering a wide-variety of complex and concurrent activities that commonly resemble distraction, for example, detecting when users are walking, running or descending staircases and simultaneously reading, eating or drinking [29], [30]. As a result, the above solutions are unable to detect or recognize a wide variety of distractions among pedestrians.…”