Childhood obesity is a critical public health problem in the United States. One factor known to contribute to childhood obesity is excess consumption. Importantly, excess consumption related to weight gain is not necessarily driven by hunger. For example, environmental food cues stimulate brain reward regions and lead to overeating even after a child has eaten to satiety. This type of cued eating is associated with increased attention to food cues; the amount of time a child spends looking at food cues (e.g., food advertisements) is associated with increased caloric intake. However, individual susceptibility to environmental food cues remains unknown. It is proposed that the prevalent practice of media multi-tasking-simultaneously attending to multiple electronic media sources-increases attention to peripheral food cues in the environment and thereby plays an important role in the development of obesity. It is hypothesized that multi-tasking teaches children to engage in constant task switching that makes them more responsive to peripheral cues, many of which are potentially harmful (such as those that promote overeating). The overarching hypothesis is that media multi-tasking alters the attentional networks of the brain that control attention to environmental cues. High media multi-tasking children are therefore particularly susceptible to food cues, thereby leading to increased cued eating. It is also predicted that attention modification training can provide a protective effect against detrimental attentional processing caused multi-tasking, by increasing the proficiency of the attention networks. These hypotheses will be tested by assessing the pathway between media-multitasking, attention to food cues, and cued eating. It will also be examined whether it is possible to intervene on this pathway by piloting an at-home attention modification training intervention designed to reduce attention to food cues. It is our belief that this research will lead to the development of low-cost, scalable tools that can train attention networks so that children are less influenced by peripheral food cues, a known cause of overeating. For example, having children practice attention modification intervention tasks regularly (which could be accomplished through user-friendly computer games or cell phone/tablet apps) might offset the negative attentional effects of media multi-tasking.
\[3/14/2020\]: Study recruitment temporarily halted due to the COVID-19 pandemic
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
SINGLE
Enrollment
92
participants will complete a sustained attention task
participants will complete multiple media tasks at the same time
participants will watch a video of media tasks being completed
Dartmouth-Hithchock Medical Center
Lebanon, New Hampshire, United States
Amount of Time Spent Looking at Food Cues While Playing a Media Game
Eye-tracking will be used to measure the amount of time spent looking at static food cues while participants play a media game on the computer. The amount time spent looking at a food cue is a measure how much attention was given to the food cue. The longer the looking time, the greater amount of attention.
Time frame: approximately 15 minutes post-intervention
Amount of Snack Foods Consumed Post-intervention
The amount of kcals consumed of snack foods after participants have completed the intervention.
Time frame: approximately 30 minutes post-intervention
Daily Usual Media Multi-tasking
Participants reported on their usual media multitasking using the short form media multitasking index. This index asks about media multitasking with other print and digital media during four primary activities: 1) watching television or movies, 2) playing video games, 3) reading books or magazines (not assigned for school), and 4) doing homework. For each activity, participants reported the frequency with which they multitasked by engaging in the other activities by using a 5-point likert scale (i.e., 0=Never, 1=Rarely, 2=Sometimes, 3=Often, 4=Always). A usual media multitasking score was computed by taking the average of the Likert response. The score ranges from 0 to 4 with a higher score indicative of higher self-reported usual media multitasking.
Time frame: approximately 10 minutes prior to the intervention
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