Combating obesity through technology and school based interventions

Over the past 12 years media multitasking has been on the rise with much of the multi-media increase being computer usage while watching television. Since media multitasking is already occurring, disrupting negative behaviors through media multitasking may lead to changes in snacking behavior and overall energy intake in adolescents and adults. Therefore, the purpose of this study was to examine the potential to disrupt snacking behavior associated with television watching through the use of multitasking. Twenty-three men and 27 women (aged 25 ± 4 y, BMI 25.2 ± 4.1 kg/m and 23 ± 4 y, BMI 25.5 ± 6.4 kg/m) participated, each completing both the control (watching a movie) and treatment (watching a movie while online chatting) activities. We observed sex differences with females consuming significantly more popcorn while chatting (P = .051) compared to males who consumed significantly more popcorn while not chatting (P = .041). Chip consumption was significantly greater (P = .026) when not chatting for all participants. We also observed weight status and sex differences with more (P<0.05) overweight and obese females consuming cookies white chatting compared to significantly more (P<0.05) overweight and obese males consuming popcorn while not chatting. Therefore media multitasking have negative implications for females, especially those who are overweight or obese. 30 Introduction Obesity has become a growing public health concern over the past three decades afflicting people of all ages and all socioeconomic groups. In the United States, overweight and obesity affects 72.3 percent of adult men and 64.1 percent of adult women (Flegal et al., 2010). One factor associated with these rising rates of overweight and obesity is screen time. The main debate around screen time and obesity is whether the different modes of screen time impact dietary intake (increased caloric intake), physical activity (increased sedentariness), or both. As adolescents transition into adulthood, time spent in moderate to vigorous physical activity declines whereas television viewing (TVV) rises (Gordon-Larsen et al., 2004). By increasing the time spent in sedentary activities, such as television viewing, the risk for obesity also increases. It has been found that men and women who engage in the most television viewing, greater than 3 hours/day, tend to have a 2-4 fold increased risk of becoming obese than those who watch the least amount of television (Bowman, 2006; Hu et al., 2003). Physical activity declines with age as well, but the relationship of this decline to screen time is less clear (Jeffery and French, 1998). On the other hand, excess calories consumed while watching television appear to be more strongly related to the risk of obesity compared to physical activity. When television is watched at high volumes it can contribute to weight gain indirectly, by increased exposure to energy-dense food advertisements, or directly through increased calories consumed. In a study conducted by Blass and colleagues (Blass et al. 2006) young adults who ate calorically-dense foods with the television on consumed more calories (793.7 kcal) compared to when the television was off (538.2 kcal). This behavior coupled with increased television viewing, increased small meal frequency and an overall decrease in physical activity can lead to a higher body mass index (BMI), 31 defined as the weight in kilograms divided by height in meters squared (kg/m) (Stroebele and de Castro, 2004). Television viewing can also influence the amount of time spent in media multitasking. The Kaiser Family Foundation reports media multitasking has increased from 16 percent in 1999 to 26 percent in 2005 with much of the multi-media increase being computer usage while watching television (Rideout, 2010). As media multitasking is on the rise, American’s are watching more television, 153 hours every month, compared to 68 hours a month on the Internet (McDonough, 2009). Since media multitasking is already occurring, disrupting negative behaviors through media multitasking may lead to changes in snacking behavior and overall energy intake in adolescents and adults. A study that examined 13 activities and the likelihood of 3 generations performing those activities separately or together found those individuals who were born between 1980 and the present (Net generation) were able to multitask more and better than individuals who were born between 1965-1979 (Generation X) and 1946-1964 (Baby Boomers). The Net generation has grown up with technology so they are more inclined to spend time instant messaging, using the Internet, playing video games, texting, eating and listening to music while doing other tasks compared to the older two generations (Carrier et al., 2009). Disrupting negative behaviors have shown to be related to television viewing may be a good target for obesity prevention. In the present study, we examined the potential to disrupt snacking behavior associated with television watching through the use of multitasking. Our hypothesis was young adults would change their snacking behavior when multitasking, in the form of online chatting, was combined with watching a movie. 32 Recruitment and Eligibility The University’s Institutional Review Board approved the study. Subjects were recruited through flyers placed around campus targeting high traffic areas including large lecture halls, on-campus cafes, and student study areas. Subjects responded to the flyers via E-mail and were provided detailed information about the study. Eligibility criteria for the study were men and women between the ages of 18-35, a BMI > 18.5, in general good health, no known major metabolic diseases (diabetes, hypoglycemia, hyperglycemia, thyroid disorder), not known to be pregnant, no allergies to any of the foods provided during the study, and were non-smokers. If the interested subjects still wished to participate, they were encouraged to select appointments at approximately the same time on two days for participation. Procedures The purpose of the study was to determine the difference in the amount of snack food consumed while watching television and multitasking (chatting) or while watching television without multitasking. However, slight deception was used by telling the subjects, “The study intends to examine the effect of a test meal on activity and behavior while watching television and chatting. ” The study consisted of two visits within one week to the Nutrition Wellness Research Center on the University’s campus. One visit served as an experimental visit and the other visit served as a control. Each participant was given an e-mail reminder to abstain from food and beverage for at least 6 hours prior to their arrival. The days were randomized according to availability between experimental and control visits. On the first visit, subjects read and signed an informed consent document. Participants were also given a physical activity, eating behavior, and screen time survey 33 and asked to provide demographic information (race, ethnicity, date of birth, education level, and occupation). Participant’s height and weight were measured with shoes removed and in light clothing using a stadiometer and scale, respectively (Seca Model 763, Sec GmbH, Hamburg, Germany). BMI was calculated from the weight in kilograms divided by the height in squared meters. At both the control and experimental visit, participants were seated in a private room, which had a couch, table, end table, and television with DVD player. For both visits participants were given a selection of eight movies, each approximately 90 minutes in length, and were allowed to select one to watch. Participants were given a standard snack of approximately 150 calories and were asked to eat it before the movie started. Four snacks of similar caloric content (120-150 calories per 30g serving) were available for the participants to consume at their leisure. These snacks included cheese crackers, cookies, popcorn, and chips and were weighed out and placed in the study room on a table next to the couch prior to the arrival of the participant. Water was also provided ad libitum. Participants were instructed not to consume any outside food or drink throughout the duration of the study. Additionally, all participants’ cell phones were removed for the study duration. Participants were fitted with four small (<15 gram) wire-free accelerometers (Msr-145S Standard Data Logger) on each thigh and upper arm. The movie choices and access to food for the experimental visit were the same as the control visit; however, participants were given a laptop and asked to chat with a member of the lab staff as well as any outside contacts they may find during the session, via Google’s GChat, using a generic account provided by the lab. The participants watched a movie of their choosing. They were encouraged to stay active on the laptop chatting and were informed that their chat content and browsing would not be recorded. 34 The chat content with the lab staff followed a script of both closedand open-ended questions of a non-sensitive nature. At the conclusion of each session the activity sensors were collected from the participant and after the subject had left, the remaining food was collected and weighed to determine the amount of each type consumed during the study. After the experimental session, a lab staff member copied and pasted the participants’ chat into a word processing program and conducted a character count and a word count to determine the volume of chatting. Copies of the chats were then deleted. Data Analysis Survey data and food consumption data were tested using a mixed-model analysis of variance (ANOVA) categorized on the basis of sex and body type (normal vs. overweight/obese). BMI and age were also used as continuous data to assess differences in survey information and food consumption using a linear model of variance (ANCOVA). Subjects were c