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Front. Psychol., 08 January 2018
Sec. Eating Behavior
This article is part of the Research Topic Eating in the Age of Smartphones: The Good, the Bad, and the Neutral View all 6 articles

To Message or Browse? Exploring the Impact of Phone Use Patterns on Male Adolescents’ Consumption of Palatable Snacks

\r\nEthan Teo&#x;Ethan Teo1†Daniel Goh&#x;Daniel Goh1†Kamalakannan M. VijayakumarKamalakannan M. Vijayakumar2Jean C. J. Liu,*Jean C. J. Liu2,3*
  • 1Raffles Institution, Singapore, Singapore
  • 2Division of Social Sciences, Yale-NUS College, Singapore, Singapore
  • 3Neuroscience and Behavioural Disorders Programme, Duke-NUS Graduate Medical School, Singapore, Singapore

Surveys of mobile phone usage suggest that adolescents habitually use their phones while eating. In this study, we explored whether the manner in which one uses a mobile phone – to engage in a social or non-social activity – can affect appetite regulation. Participants were fifty male adolescents randomly assigned to engage in one of the following phone-based activities: (1) sending and receiving messages (social activity), or (2) reading a neutral article (non-social activity). When given the opportunity to snack, participants in the messaging group consumed more snacks that those who read the article. Our findings correspond to a large literature emphasizing social influences on food intake, and suggest that phone use patterns may predispose an individual to overeating.


Within the span of a decade, smartphones have permeated almost every aspect of our daily lives. Young adults report multi-tasking with their phones: in the restroom, during bedtime, waiting at a red light, and during meal-times (Webby Awards, 2015). Indeed, for one in four adolescents, phone use is a near-constant activity (Lenhart, 2015). Reflecting on this technological landscape, there have been recent efforts to develop guidelines for the use of mobile phones – particularly for the pediatric population growing up with ready access to smartphones (American Academy of Pediatrics, 2015; Radesky et al., 2015).

Phone Use in an Obesogenic Environment

In the discussion of guidelines, one area of concern is the extent to which mobile phones may contribute to the obesogenic environment, predisposing children and adolescents to weight gain (Swinburn et al., 1999; AAP Council on Communications and Media, 2016a; Reid Chassiakos et al., 2016). Here, an analogy can be made to other forms of technology such as television and video games. For example, the increased consumption of television has been found to predict a higher body mass index and greater adiposity amongst children and adolescents (Coon and Tucker, 2002; Janz et al., 2002; Staiano et al., 2013). When given the opportunity to eat, those who do so while playing video games or watching television also show greater food intake (Temple et al., 2007; Chaput et al., 2011). Finally, interventions to decrease the use of television, videotapes, and video games have been successful in reducing the body mass index of school children (Robinson, 1999). Taken together, the current evidence suggests that using these technologies – collectively referred to as ‘screen time’ (for devices involving a screen) – constitutes a risk factor for obesity.

Although corresponding evidence for mobile phones is lacking, the American Academy of Pediatrics (2017) has classified phone usage as ‘screen time,’ generalizing findings from television and video games to mobile phones. This is reasonable in the discussion of weight management, since phone use – like other forms of screen use – is a sedentary activity (Lanningham-Foster et al., 2006). Additionally, multi-tasking with one’s phone has been found to be a distractor for tasks ranging from reading an article to crossing the road (Stavrinos et al., 2009; Chen and Yan, 2016). Since the primary account of why screen time promotes eating is that it distracts the user from satiety signals (Bellisle et al., 2004; Brunstrom and Mitchell, 2006; Hetherington et al., 2006; Robinson et al., 2013), multi-tasking with one’s phone can likewise be expected to increase food intake.

Exploring the Social Nature of Phone Use

Beyond distraction, however, a key difference between smartphones and traditional forms of digital screens is that phone use is inherently social. Studies of phone use patterns consistently identify messaging functions as the top feature used in mobile phones (Lenhart, 2015; Smith, 2015), with adolescents estimating that they send 118 messages each day (Rideout et al., 2010). One implication of this usage pattern is that adolescents – when multi-tasking with their phones while eating – interact with friends and family in a way that they do not when multi-tasking with television or video games (with the exception of multi-player games).

The social nature of phone use is significant because individuals eat more with friends and family than they do alone – a phenomenon known as ‘social facilitation’ (de Castro and de Castro, 1989; de Castro, 1997; Herman, 2015). The mere company of one person can increase food intake by 44% (de Castro and de Castro, 1989; de Castro, 1997), with facilitation effects so robust that they have been observed: regardless of a person’s homeostatic hunger (de Castro and de Castro, 1989), regardless of the time and place of eating (de Castro et al., 1990), across groups of various cultures and demographics (Feunekes et al., 1995; de Castro et al., 1997), and across diverse study methodologies (Klesges et al., 1984; Berry et al., 1985; see de Castro, 1997; Herman et al., 2003; and Herman, 2015 for reviews of this literature).

Given the ubiquitous nature of social facilitation, a corollary question is whether phone-based messaging confers a risk for overeating – over and above the potential for phone use to distract the user. Although facilitation effects have traditionally been observed in the physical presence of other people, research on non-eating behaviors suggests that virtual presence may be sufficient (with social facilitation broadly defined here as the promotion of a dominant response; Zajonc, 1965). Thus, the virtual company of another person has been found to facilitate tasks ranging from anagrams, mazes, arithmetic, to exercise (Park and Catrambone, 2007; Anderson-Hanley et al., 2011; Snyder et al., 2012). Extending these findings, we investigated whether the virtual presence of friends and family – connected via phone-based messaging – would likewise result in the social facilitation of eating.

The Current Study

To address this question, we conducted a randomized controlled trial monitoring the food intake of adolescents given the opportunity to snack. All participants used a mobile phone while eating, and differed only in how the phone was used: to engage in the social activity of sending and receiving messages (messaging group), or to carry out the non-social activity of reading a neutral article (control group). We hypothesized that messaging would result in the increased consumption of palatable snacks.

Materials and Methods


Participants were 50 male adolescents enrolled in Years 7–10 of an all-boys public school in Singapore (mean age: 14.64 years; SD: 0.75). We chose to recruit male participants as gender has been found to moderate phone use (Lenhart, 2015), eating behaviors (Wardle et al., 2004), and the relationship between technology and eating behaviors (Robinson and Killen, 1995); as such, including both genders would have required a much larger sample size. The study was conducted as part of the school’s research education program, and participants responded to school-wide advertisements inviting them to the study.

After written assent and written informed parental consent were obtained, participants were randomly allocated to either the messaging or control group. The two groups did not differ in age, ethnicity, body mass index, or baseline eating behavior (Table 1). All procedures were approved by the National University of Singapore’s Institutional Review Board (#A-15-170). All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the National University of Singapore.


TABLE 1. Baseline characteristics of participants allocated to the messaging and control groups.


Baseline Questionnaires

As a measure of baseline eating behavior, we administered the Dutch Eating Behavior Questionnaire (DEBQ; van Strien et al., 1986). This questionnaire assessed whether participants ate based on: external rather than internal cues (‘external eating’), emotions (‘emotional eating’), or concerns to restrict one’s eating (‘restrained eating’). Reliability for each of the subscales was acceptable (Cronbach’s alpha for external eating = 0.77; emotional eating = 0.94; restrained eating = 0.88).

Additionally, we administered a questionnaire investigating participants’ use of social networking platforms. This asked participants which mobile phone they used, the number of friends they had on their phone contact list, the number of messages they sent each day, which social networking platforms they used, what they used their phone for, and whether they used their phone in everyday settings (in bed, in the toilet, during meals, in class, during commute, and during idle times).

Snack Food

For the snack food, we placed 50 g of chicken-flavored ‘Twisties’ (266 kcals; Mondelez International) in an unlabeled bowl. This corn puff snack was chosen because: (i) it is popular with adolescents, (ii) can be found in school vending machines, and (iii) comes in small regular-sized pieces. Pilot tests with a sample of students confirmed that the snack was palatable and that the portion size (50 g) exceeded what a typical student would consume in one setting.


Each experimental session took place at the end of a school day (mid-afternoon) and lasted for approximately 30 min. The set-up was intended to mimic what participants would typically encounter – the opportunity to eat highly palatable snacks following a day of school. On average, participants reported having eaten 4.5 h (SD: 2.8 h) before arrival (Table 1).

As the cover story, participants were made to believe that the researchers were interested in how technology influenced health. After completing baseline questionnaires, participants were told to bring out their mobile phones and to follow the experimenter’s instructions; additionally, they were told that they should not engage in any other activity with their phones. Compliance with phone use instructions was monitored through surreptitious observation from a distance.

In the messaging group, participants were asked to access the phone-based instant messaging service ‘WhatsApp.’ Within WhatsApp, participants identified an active chat group comprising of at least 10 users, and engaged in this group chat for a 10-min duration. Mimicking real-life situations, participants were given no other instructions regarding whom they should communicate with nor what they should discuss.

In the control group, participants were asked to access a neutral article sent to them via email. This was chosen to approximate web-browsing activities, implicated in phone use surveys as the top non-social function used on mobile phones (Rainie and Zickuhr, 2015). The article discussed a neutral topic (the immune system; MacPherson and Austyn, 2012), and was longer than what a typical student could finish reading during the session; additionally, 2 year 9 students who did not participate in the study assessed the article to be easy to read and neutral in tone. In short, this condition was comparable to previous distraction manipulations that had been found to increase food intake (e.g., listening to audio stories, listening to music, watching television; Bellisle and Dalix, 2001; Bellisle et al., 2004; Stroebele and de Castro, 2006; Long et al., 2011), and was designed to control for any distracting effects of mere phone use. Participants in this group read the article on their phones for a 10-min duration.

Across both conditions, the opportunity to eat was introduced in a casual manner. The bowl of snack food was left on the table throughout the 10 min, and the experimenter informed participants that the food was leftovers they were free to consume at will. At the end of the 10 min, participants were debriefed about the true aims of the study.

Data Analyses

As the primary analysis, we ran an independent samples t-test comparing the amount of food consumed by participants in the messaging and control groups. The Type 1 error rate was controlled at α = 0.05, and power calculations showed that there was statistical power at the recommended 0.80 level to detect a large effect size (d = 0.80, comparable to effect sizes observed in previous social facilitation studies; Herman, 2015). All analyses were conducted using SPSS (IBM Corp., 2017) & R (R Core Team, 2017).


Participants’ Baseline Patterns of Phone Usage

At baseline, 48% of participants reported regular use of their phones during meal-times (Table 2). Participants were most likely to use the messaging functions of their phones (Table 3), with 50% of participants sending at least 41 messages daily (Table 4). Together, these statistics suggest that the phenomenon being studied – texting while eating – is one participants themselves have likely engaged in on a regular basis.


TABLE 2. Messaging and control participants’ self-reported mobile phone usage during common activities.


TABLE 3. Messaging and control participants’ self-reported use of mobile phone functions.


TABLE 4. Messaging and control participants’ frequency of sending mobile phone messages each day.

Food Intake as a Function of Experimental Condition

Primary Analyses

As shown in Figure 1, participants in the messaging group consumed 58% more snacks than those in the control group [t(48) = -4.68, p < 0.001, d = 1.32]. The 95% confidence interval suggests that this corresponded to an average increase of 29.19–73.14 kcals consumed.


FIGURE 1. Snack intake of participants in the messaging and control groups; vertical lines represent 1 standard error of the means (p < 0.001).

Accounting for Baseline Eating Behaviors

As a follow-up, we conducted a stepwise multiple regression to assess the influence of messaging after controlling for baseline eating behaviors. In Step 1, a model including: scores on the DEBQ (external, emotional, and restrained eating) and the time interval from the previous meal accounted for 5.2% of the variance in food intake, F(4,41) = 0.56, p = 0.69. Adding participants’ experimental condition in Step 2 explained a further 28.4% of the variance – a statistically significant increase [F(1,40) = 17.11, p < 0.001].

Were Participants Primed or Distracted When Reading an Article?

Thus far, our results are consistent with the hypothesis that messaging activities would increase food intake relative to reading an article. However, an alternative account is the reverse – that reading the article reduced control participants’ snack consumption instead. This may have occurred if, instead of distracting participants, the topic of the article (the immune system) primed participants to eat in a healthy manner. This, in turn, may have caused them to eat fewer snacks.

Whereas distraction effects have been observed across various groups and situations, the influence of health primes is not universal (Forwood et al., 2015), affecting primarily dieters for whom primes reinforce their goals (Papies and Hamstra, 2010; Buckland et al., 2013; Papies, 2016). Correspondingly, if our article did indeed prime participants, those with higher dietary restraint would be more likely to show reduced food intake than those with low restraint. To this end, we ran a Pearson’s correlation between snack intake and DEBQ restraint scores amongst participants in the control condition. This correlation did not approach statistical significance [r(20) = 0.02, p = 0.92]. Similarly, amongst participants who had read the article, there was no significant difference in food intake between those with restraint scores at or above the median (≥2.3), as compared to those with scores below the median, [t(20) = 0.33, p = 0.74]. Taken together, we were unable to replicate a commonly observed pattern in the health priming literature, and found no evidence that priming mechanisms were at play.


In this study, we described the impact of smartphone messaging activities on appetite regulation. In line with research emphasizing social influences on food intake (de Castro, 1997; Herman, 2015), we found that male adolescents who sent and received text messages consumed more palatable snacks than those who used their phones to read an article. The difference between these two activities accounted for a third of the variance in snack consumption, and was a larger influence than time since the last meal or individual differences in eating behaviors (as measured by the DEBQ). To our knowledge, this is the first demonstration of how specific patterns of phone usage may predispose adolescents to over-eating.

A Case for Virtual Social Facilitation

In terms of theory, our findings are consistent with ‘virtual social facilitation.’ Outside the field of ingestive behavior, several studies have found that social influence is so pervasive that computer-based or online presence is sufficient to elicit facilitation effects (Park and Catrambone, 2007; Anderson-Hanley et al., 2011; Snyder et al., 2012). Our study extends these findings to the eating domain, suggesting that the mere online presence of friends and family is able to promote eating behaviors.

At the same time, we caution that virtual social facilitation remains a nascent concept that requires follow-up. For example, the effects we observed do not fit neatly into current theories. By convention, social facilitation is classified based on what others are doing (Zajonc, 1965): ‘co-actors’ who are also eating cause the familiar increase in food intake, but a ‘passive (non-eating) audience’ renders the individual self-conscious – leading to a decrease in food consumption (Herman, 2015). With mobile phones, however, whomever one messages may not be a co-actor who is also eating. Similarly, message recipients are not privy to how much one eats, minimizing the need to maintain an impression via food intake. Accordingly, virtual company cannot be described to have either co-action or passive audience effects, and future research will need to investigate whether current accounts of social facilitation apply to the digital realm.

Ruling Out Distraction Accounts

To strengthen the case for virtual social facilitation, future research will also need to rule out a solely cognitive explanation of our results. As described in the introduction, the primary account for why screen use affects food intake is that it diverts attention from the act of eating; with diminished cognitive resources, the screen-user engages in ‘mindless eating’ and consumes more (Ogden et al., 2013; Dohle et al., 2017). Although distraction effects were addressed through a control group engaged in a non-social phone activity, it remains possible that our activity – reading an article – was not as distracting to participants as messaging was. To the extent this was true, participants in the messaging group may have simply eaten more because they were more distracted (Bellisle et al., 2004; Brunstrom and Mitchell, 2006; Hetherington et al., 2006; Robinson et al., 2013) – rather than because the act of messaging was social in nature. Further studies are needed to tease apart these accounts by including other phone-use conditions (e.g., playing a solitary game), or by assessing cognitive resources required for messaging versus reading (e.g., through dual task paradigms).

Toward Evidence-Based Guidelines on Pediatric Phone Use

More broadly, our findings add to the ongoing discussion of how technology contributes to the obesogenic environment. Beyond guidelines on whether or not digital screens should be used during meal-times (AAP Council on Communications and Media, 2016a,b), we found that the manner in which one uses a mobile phone can compound the problem of over-eating. This research is timely as our own participants reported the habitual use of mobile phones during a meal. While urging replication of our work, we tentatively suggest that switching from one of these activities (messaging) to the other (browsing and reading) could reduce the consumption of palatable snacks amongst adolescents.

Study Limitations

Although we discuss the potential implications of our study, we highlight several limitations. First, our participants came from a homogenous all-boys school, and the extent to which these results generalize to other populations is unknown. Second, we chose to use an experimental design such that causality can be inferred. However, this required us to make several design choices that could limit generalizability. For example, we modeled our design on an everyday scenario where adolescents have the opportunity to snack after school. In so doing, we were focusing on the hedonic drive to eat, and are unclear whether similar results will be found when food intake is more strongly driven by homeostatic concerns (e.g., in a breakfast meal after an overnight fast; Lutter and Nestler, 2009). Similarly, in striving for ecological validity, we allowed participants in the messaging group to converse freely. This meant that we had little control over discussion topics, and cannot preclude the possibility that participants discussed the experiment in their chat groups (and perhaps were encouraged by their friends to eat). Finally, in the control condition, we opted to have participants use their phones for a non-social activity – reading an article. Although our analyses suggest that the article was unlikely to have attenuated food consumption (e.g., by priming a health message), we cannot rule out this possibility in the absence of a no-phone condition. In light of these limitations, we suggest that future research extend our findings through alternate operationalization of the experimental conditions. The use of diary or epidemiological designs would also allow the true impact of phone activities to be estimated amongst free-living adolescents.


Our study was motivated by the observation that smartphones provide unprecedented opportunities for adolescents to connect with friends and family. Although the social feature of phones can have beneficial effects (Reid Chassiakos et al., 2016), choosing to message while eating can promote the overconsumption of food. Over time, this may predispose adolescents to weight gain, and is a potential risk factor that requires further study.

Author Contributions

ET and DG conceptualized and designed the study, collected the data, drafted the initial manuscript, and approved the final manuscript as submitted. KV and JL conceptualized and designed the study, carried out statistical analyses, reviewed and revised the manuscript, and approved the final manuscript as submitted.


This work was supported by a grant awarded to Dr. Jean Liu by the Singapore Ministry of Education (AcRF Tier 1 / Yale-NUS Internal Grant IG15-LR052).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The authors gratefully acknowledge Mr. Sylvester Chan for his support of this project.


AAP Council on Communications and Media (2016a). Media and young minds. Pediatrics 138:e20162591. doi: 10.1542/peds.2016-2591

PubMed Abstract | CrossRef Full Text

AAP Council on Communications and Media (2016b). Media use in school-aged children and adolescents. Pediatrics 138:e20162592.

Google Scholar

American Academy of Pediatrics (2015). “Children, adolescents, and media on the AAP agenda for children,” in Growing up Digital: Media Research Symposium. Symposium Conducted at the Meeting of the American Academy of Pediatrics, ed. D. Shifrin (Rosemont, IL: American Academy of Pediatrics).

American Academy of Pediatrics (2017). Family Media Plan. Available at: [accessed August 31, 2017].

Anderson-Hanley, C., Snyder, A. L., Nimon, J. P., and Arciero, P. J. (2011). Social facilitation in virtual reality-enhanced exercise: competitiveness moderates exercise effort of older adults. Clin. Interv. Aging 6, 275–280. doi: 10.2147/CIA.S25337

PubMed Abstract | CrossRef Full Text | Google Scholar

Bellisle, F., and Dalix, A.-M. (2001). Cognitive restraint can be offset by distraction, leading to increased meal intake in women. Am. J. Clin. Nutr. 74, 197–200.

PubMed Abstract | Google Scholar

Bellisle, F., Dalix, A.-M., and Slama, G. (2004). Non food-related environmental stimuli induce increased meal intake in healthy women: comparison of television viewing versus listening to a recorded story in laboratory settings. Appetite 43, 175–180. doi: 10.1016/j.appet.2004.04.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Berry, S. L., Beatty, W. W., and Klesges, R. C. (1985). Sensory and social influences on ice cream consumption by males and females in a laboratory setting. Appetite 6, 41–45. doi: 10.1016/S0195-6663(85)80049-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Brunstrom, J. M., and Mitchell, G. L. (2006). Effects of distraction on the development of satiety. Br. J. Nutr. 96, 761–769.

PubMed Abstract | Google Scholar

Buckland, N. J., Finlayson, G., and Hetherington, M. M. (2013). Pre-exposure to diet-congruent food reduces energy intake in restrained dieting women. Eat. Behav. 14, 249–254. doi: 10.1016/j.eatbeh.2013.03.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Chaput, J.-P., Visby, T., Nyby, S., Klingenberg, L., Gregersen, N. T., Tremblay, A., et al. (2011). Video game playing increases food intake in adolescents: a randomized crossover study. Am. J. Clin. Nutr. 93, 1196–1203. doi: 10.3945/ajcn.110.008680

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, Q., and Yan, Z. (2016). Does multitasking with mobile phones affect learning? A review. Comput. Human Behav. 54, 34–42. doi: 10.1016/j.chb.2015.07.047

CrossRef Full Text | Google Scholar

Coon, K. A., and Tucker, K. L. (2002). Television and children’s consumption patterns. A review of the literature. Minerva Pediatr. 54, 423–436.

Google Scholar

de Castro, J. M. (1997). Socio-cultural determinants of meal size and frequency. Br. J. Nutr. 77(Suppl. 1), S39–S55. doi: 10.1079/BJN19970103

PubMed Abstract | CrossRef Full Text | Google Scholar

de Castro, J. M., Bellisle, F., Feunekes, G. I. J., Dalix, A. M., and DeGraaf, C. (1997). Culture and meal patterns: a comparison of the food intake of free-living American, Dutch, and French students. Nutr. Res. 17, 807–829. doi: 10.1016/S0271-5317(97)00050-X

CrossRef Full Text | Google Scholar

de Castro, J. M., Brewer, E. M., Elmore, D. K., and Orozco, S. (1990). Social facilitation of the spontaneous meal size of humans occurs regardless of time, place, alcohol or snacks. Appetite 15, 89–101. doi: 10.1016/0195-6663(90)90042-7

CrossRef Full Text | Google Scholar

de Castro, J. M., and de Castro, E. S. (1989). Spontaneous meal patterns of humans: influence of the presence of other people. Am. J. Clin. Nutr. 50, 237–247.

PubMed Abstract | Google Scholar

Dohle, S., Diel, K., and Hofmann, W. (2017). Executive functions and the self-regulation of eating behavior: a review. Appetite doi: 10.1016/j.appet.2017.05.041 [Epub ahead of print].

PubMed Abstract | CrossRef Full Text | Google Scholar

Feunekes, G. I. J., Degraaf, C., and Vanstaveren, W. A. (1995). Social facilitation of food-intake is mediated by meal duration. Physiol. Behav. 58, 551–558. doi: 10.1016/0031-9384(95)00087-Y

CrossRef Full Text | Google Scholar

Forwood, S. E., Ahern, A. L., Hollands, G. J., Ng, Y.-L., and Marteau, T. M. (2015). Priming healthy eating. You can’t prime all the people all of the time. Appetite 89, 93–102. doi: 10.1016/j.appet.2015.01.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Herman, C. P. (2015). The social facilitation of eating. A review. Appetite 86, 61–73. doi: 10.1016/j.appet.2014.09.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Herman, C. P., Roth, D. A., and Polivy, J. (2003). Effects of the presence of others on food intake: a normative interpretation. Psychol. Bull. 129, 873–886. doi: 10.1037/0033-2909.129.6.873

PubMed Abstract | CrossRef Full Text | Google Scholar

Hetherington, M. M., Anderson, A. S., Norton, G. N. M., and Newson, L. (2006). Situational effects on meal intake: a comparison of eating alone and eating with others. Physiol. Behav. 88, 498–505. doi: 10.1016/j.physbeh.2006.04.025

PubMed Abstract | CrossRef Full Text | Google Scholar

Janz, K. F., Levy, S. M., Burns, T. L., Torner, J. C., Willing, M. C., and Warren, J. J. (2002). Fatness, physical activity, and television viewing in children during the adiposity rebound period: the Iowa bone development study. Prev. Med. 35, 563–571. doi: 10.1006/pmed.2002.1113

PubMed Abstract | CrossRef Full Text | Google Scholar

Klesges, R. C., Bartsch, D., Norwood, J. D., Kautzman, D., and Haugrud, S. (1984). The effects of selected social and environmental variables on the eating behavior of adults in the natural-environment. Int. J. Eat. Disord. 3, 35–41. doi: 10.1002/1098-108X(198422)3:4<35::AID-EAT2260030405>3.0.CO;2-7

CrossRef Full Text | Google Scholar

Lanningham-Foster, L., Jensen, T. B., Foster, R. C., Redmond, A. B., Walker, B. A., Heinz, D., et al. (2006). Energy expenditure of sedentary screen time compared with active screen time for children. Pediatrics 118, e1831–e1835. doi: 10.1542/peds.2006-1087

PubMed Abstract | CrossRef Full Text | Google Scholar

Lenhart, A. (2015). Teens, Social Media and Technology Overview. Washington, DC: Pew Research Center.

Google Scholar

Long, S., Meyer, C., Leung, N., and Wallis, D. J. (2011). Effects of distraction and focused attention on actual and perceived food intake in females with non-clinical eating psychopathology. Appetite 56, 350–356. doi: 10.1016/j.appet.2010.12.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Lutter, M., and Nestler, E. J. (2009). Homeostatic and hedonic signals interact in the regulation of food intake. J. Nutr. 139, 629–632. doi: 10.3945/jn.108.097618

PubMed Abstract | CrossRef Full Text | Google Scholar

MacPherson, G., and Austyn, J. (eds). (2012). “The immune system”, in Exploring Immunology: Concepts and Evidence (Weinheim: Wiley-Blackwell), 5–47.

Ogden, J., Coop, N., Cousins, C., Crump, R., Field, L., Hughes, S., et al. (2013). Distraction, the desire to eat and food intake. Toward an expanded model of mindless eating. Appetite 62, 119–126. doi: 10.1016/j.appet.2012.11.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Papies, E. K. (2016). Goal priming as a situated intervention tool. Curr. Opin. Psychol. 12, 12–16. doi: 10.1016/j.copsyc.2016.04.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Papies, E. K., and Hamstra, P. (2010). Goal priming and eating behavior: enhancing self-regulation by environmental cues. Health Psychol. 29, 384–388. doi: 10.1037/a0019877

PubMed Abstract | CrossRef Full Text | Google Scholar

Park, S., and Catrambone, R. (2007). Social facilitation effects of virtual humans. Hum. Factors 49, 1054–1060. doi: 10.1518/001872007X249910

PubMed Abstract | CrossRef Full Text | Google Scholar

Smith, A. (2015). U.S. Smartphone Use in 2015. Washington, DC: Pew Research Center.

Google Scholar

Radesky, J., Schumacher, J., and Zuckerman, B. (2015). Mobile and interactive media use by young children: the good, the bad, and the unknown. Pediatrics 135, 1–3. doi: 10.1542/peds.2014-2251

PubMed Abstract | CrossRef Full Text | Google Scholar

Rainie, L., and Zickuhr, K. (2015). Americans’ Views on Mobile Etiquette. Washington, DC: Pew Research Center.

Google Scholar

Reid Chassiakos, Y. L., Radesky, J., Christakis, D., Moreno, M. A., and Cross, C. (2016). Children and adolescents and digital media. Pediatrics 138:e20162593. doi: 10.1542/peds.2016-2593

PubMed Abstract | CrossRef Full Text | Google Scholar

Rideout, V. J., Foehr, U. G., and Roberts, D. F. (2010). Generation M2: Media in the Lives of 8- to 18-Year-Olds. Menlo Park, CA: Kaiser Family Foundation.

Google Scholar

Robinson, E., Aveyard, P., Daley, A., Jolly, K., Lewis, A., Lycett, D., et al. (2013). Eating attentively: a systematic review and meta-analysis of the effect of food intake memory and awareness on eating. Am. J. Clin. Nutr. 97, 728–742. doi: 10.3945/ajcn.112.045245

PubMed Abstract | CrossRef Full Text | Google Scholar

Robinson, T. N. (1999). Reducing children’s television viewing to prevent obesity: a randomized controlled trial. JAMA 282, 1561–1567. doi: 10.1001/jama.282.16.1561

CrossRef Full Text | Google Scholar

Robinson, T. N., and Killen, J. D. (1995). Ethnic and gender differences in the relationships between television viewing and obesity, physical activity, and dietary fat intake. J. Health Educ. 26, S91–S98. doi: 10.1080/10556699.1995.10603155

CrossRef Full Text | Google Scholar

Snyder, A. L., Anderson-Hanley, C., and Arciero, P. J. (2012). Virtual and live social facilitation while exergaming: competitiveness moderates exercise intensity. J. Sport Exerc. Psychol. 34, 252–259. doi: 10.1123/jsep.34.2.252

PubMed Abstract | CrossRef Full Text | Google Scholar

Staiano, A. E., Harrington, D. M., Broyles, S. T., Gupta, A. K., and Katzmarzyk, P. T. (2013). Television, adiposity, and cardiometabolic risk in children and adolescents. Am. J. Prev. Med. 44, 40–47. doi: 10.1016/j.amepre.2012.09.049

PubMed Abstract | CrossRef Full Text | Google Scholar

Stavrinos, D., Byington, K. W., and Schwebel, D. C. (2009). Effect of cell phone distraction on pediatric pedestrian injury risk. Pediatrics 123, e179–e185. doi: 10.1542/peds.2008-1382

PubMed Abstract | CrossRef Full Text | Google Scholar

Stroebele, N., and de Castro, J. M. (2006). Listening to music while eating is related to increases in people’s food intake and meal duration. Appetite 47, 285–289. doi: 10.1016/j.appet.2006.04.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Swinburn, B., Egger, G., and Raza, F. (1999). Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Prev. Med. 29, 563–570. doi: 10.1006/pmed.1999.0585

PubMed Abstract | CrossRef Full Text | Google Scholar

Temple, J. L., Giacomelli, A. M., Kent, K. M., Roemmich, J. N., and Epstein, L. H. (2007). Television watching increases motivated responding for food and energy intake in children. Am. J. Clin. Nutr. 85, 355–361.

PubMed Abstract | Google Scholar

van Strien, T., Frijters, J. E. R., Bergers, G. P. A., and Defares, P. B. (1986). The Dutch Eating Behavior Questionnaire (DEBQ) for assessment of restrained, emotional, and external eating behavior. Int. J. Eat. Disord. 5, 295–315. doi: 10.1002/1098-108X(198602)5:2<295::AID-EAT2260050209>3.0.CO;2-T

CrossRef Full Text | Google Scholar

Wardle, J., Haase, A. M., Steptoe, A., Nillapun, M., Jonwutiwes, K., and Bellisie, F. (2004). Gender differences in food choice: the contribution of health beliefs and dieting. Ann. Behav. Med. 27, 107–116. doi: 10.1207/s15324796abm2702_5

PubMed Abstract | CrossRef Full Text | Google Scholar

Webby Awards (2015). 2015 Annual Trend Report. Ashland, TN: International Academy of Digital Arts and Sciences.

Zajonc, R. B. (1965). Social facilitation. Science 149, 269–274. doi: 10.1126/science.149.3681.269

CrossRef Full Text | Google Scholar

Keywords: technology, screen use, social facilitation, obesity, appetite

Citation: Teo E, Goh D, Vijayakumar KM and Liu JCJ (2018) To Message or Browse? Exploring the Impact of Phone Use Patterns on Male Adolescents’ Consumption of Palatable Snacks. Front. Psychol. 8:2298. doi: 10.3389/fpsyg.2017.02298

Received: 02 November 2017; Accepted: 18 December 2017;
Published: 08 January 2018.

Edited by:

Boris C. Rodríguez-Martín, Fundación Recal, Spain

Reviewed by:

Jason Michael Thomas, Aston University, United Kingdom
Yunier Broche-Pérez, University “Marta Abreu” of Las Villas, Cuba

Copyright © 2018 Teo, Goh, Vijayakumar and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jean C. J. Liu,

Joint first author

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