Electronic device use and beverage related sugar and caffeine intake in US adolescents


Authors: Kelly M. Bradbury aff001;  Ofir Turel aff002;  Katherine M. Morrison aff001
Authors place of work: Department of Pediatrics, Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada aff001;  Department of Information Systems and Decision Sciences, Mihaylo College of Business and Economics, California State University—Fullerton, Fullerton, California, United States of America aff002
Published in the journal: PLoS ONE 14(10)
Category: Research Article
doi: 10.1371/journal.pone.0223912

Summary

Background

Despite recent declines in consumption of sugary beverages, energy drinks (ED) and sodas continue to contribute a substantial amount of sugar and caffeine to the diet of youth. Consumption of these beverages has been linked with electronic device use, however in-depth associations between sugar and caffeine intake from energy drinks and sodas with various electronic devices are not clear.

Objective

Describe the relationship of soda and energy drink consumption and associated added sugar and caffeine intake with electronic device use among adolescents.

Methods

Secondary data from the 2013–2016 cycles of Monitoring the Future Survey, a national, repeated, cross-sectional study, were analyzed. Information on energy drink and soda consumption by students in grades 8 and 10 (n = 32,418) from 252–263 schools randomly sampled from all US states was used.

Results

Soda and energy drink consumption decreased each year from 2013–2016 while daily use of electronic devices remained stable. An additional hour/day of TV was linked to a 6.92g (6.31,7.48; p<0.001) increase in sugar intake and a 32% (OR = 1.32; 1.29,1.35; p < .001) higher risk of exceeding World Health Organization (WHO) recommended sugar intakes. Further, each hour/day of TV was linked to a 28% increased risk of exceeding caffeine recommendations (OR = 1.25–1.31; p<0.001). Each hour per day talking on a cellphone was associated with an increased risk of exceeding WHO sugar and caffeine intakes by 14% (OR = 1.11–1.16; p<0.001) and 18% (OR = 1.15–1.21; p<0.001) respectively. Video game use was only weakly linked to caffeine intake. Computer use for school was associated with lower likelihood of exceeding sugar intake cut-offs.

Conclusion

While a trend towards reduced energy drink and soda intake from 2013–2016 was evident, greater electronic device use, especially TV time, was linked to higher intake of beverage-derived added sugar and caffeine amongst adolescents. Addressing these behaviours through counselling or health promotion could potentially help to reduce excess sugar and caffeine intake from sodas and energy drinks among this population.

Keywords:

Adolescents – Beverages – Caffeine – Diet – Electronics – Schools – Social media – Video games

Introduction

Consumption of sugar-sweetened beverages (SSB) such as sodas is linked to adverse health consequences including obesity [13] and various metabolic health impacts [4, 5] including dyslipidemia [6], diabetes [7], dental caries [8] and poor sleep hygiene [9, 10] . Artificially-sweetened beverages are also associated with higher energy intake despite their calorie-free properties [11], possibly due to their heightened sweetness which may impact one’s perception of energy intake [12]. It is recommended that SSB and artificially-sweetened beverage intake be limited among youth [11, 13, 14] given these associated health impacts.

Among SSB, energy drinks contain high amounts of added sugar or artificial sweeteners and typically more caffeine than sodas [15]. Consumption of excess caffeine is associated with adverse health impacts including difficulties with sleep, headaches, elevated blood pressure, nausea, vomiting, diarrhea and chest pain [15, 16]. Consequently, both the American Academy of Pediatrics [17] and the Canadian Pediatric Society [15] have released position statements which urge pediatricians to educate youth and families on the potential risks of these beverages and recommend against consumption of these drinks.

SSB [18, 19] and artificially-sweetened beverage intakes have declined since the early 2000’s [19] and energy drink consumption has remained relatively stable among children and adolescents [20]. Despite these declines, a recent review of large national cohort studies found that SSB, including beverages such as energy drinks and sodas, remain a top contributor to daily energy intake among children and adolescents aged 2–19 years, with an estimated 130 calories consumed per day from these beverages [19]. Given that both the World Health Organization (WHO) [21] and the Dietary Guidelines for Americans 2015–2020 [14] recommend that no more than 10% of energy intake should come from added sugars in food or beverages, sodas and energy drinks are important sources of added sugar to consider. Furthermore, investigations on caffeine intake from beverage sources among youth demonstrate that energy drinks generally contribute approximately 5–7% of the total daily caffeine intake of adolescents and young adults, while carbonated drinks remain a top contributor to total daily caffeine intake for children and adolescents at 17% [20]. Thus, consuming these beverages may increase the likelihood of failing WHO recommendations which can lead to adverse health outcomes including dental caries and obesity [21, 22].

Youth generally report consuming energy drinks for their taste and their stimulatory effect [16], and some have hypothesized that energy drink consumption may explain the association of video game usage and poorer sleep hygiene [10, 16, 23]. Soda and energy drink consumption may also be influenced by advertisements online [24, 25] and distracted eating; something that can occur during electronic device usage. These result in adverse impacts on overall diet quality and energy intake in children and adults [26, 27]. Adults consume a greater caloric intake from snacking while watching TV alone compared to when they are socializing. Further, a randomized crossover trial in adolescent boys demonstrated that those who played video games for one hour had a significantly higher energy intake from ad libitum lunches provided afterwards compared to those who spent time resting for one hour [26, 28]. While the latter study measured energy intake after video game use, multiple observational studies have also associated energy drink and SSB intake with increased time spent on electronics [29, 30], including video games [3133] and television watching [23, 31, 32, 3436]. Given that SSB, including sodas and energy drinks continue to contribute a substantial amount of energy and caffeine to the youth diet, investigating the link between their consumption with multiple forms of electronic device usage in a large national representative population may help establish a deeper understanding of this relationship.

The objective of this study was to examine potential links between energy drink and soda consumption with use of electronic devices amongst adolescents. Due in part to the distracting properties of electronic device use, we hypothesized that caffeine and added sugar intakes from beverage sources would be directly associated with electronic device use time. Additionally, given the needed stimulation and concentration for video gaming, and marketing campaigns that target video gamers, we expected a particularly strong association between energy drink and caffeine intake with video game usage.

Methods

Study population

Participants were students in grades 8 and 10 who completed the 2013–2016 cycles of the Monitoring the Future study [37, 38]. The Monitoring the Future project relies on anonymized survey data to assist policymakers in understanding youth attitudes, beliefs and behaviours including alcohol and drug use (Funded by the National Institutes of Health; more information available at: http://www.monitoringthefuture.org/purpose.html). The current analysis is a secondary analysis of fully anonymized data made available from the Monitoring the Future Study through an application process with co-author OT. The Monitoring the Future study has an application process for secondary data analysis on de-identified data that does not require additional reviews by ethics boards outside of the IRB at Michigan.

The Monitoring the Future study is carried out in schools, is voluntary and parents and students are given the opportunity to decline participation. All procedures are reviewed and approved on an annual basis by the University of Michigan’s Institutional Review Board for compliance with federal guidelines for the treatment of human subjects [38]. This study is based on a survey, is nationally representative and incorporates repeated cross-sectional studies in youth (i.e. randomly sampled schools each year). Details of the study conduct are reported elsewhere [39]. Data from 252–263 participating schools from 2013 through 2016 were used, and the annual population sizes varied from 15,015–17,643 students in 8th grade (13–14 years old) and 13,262–16,147 students in 10th grade (15–16 years old). Depending on the cycle year, response rates varied between 87% and 90% [38].

The full sample included 152,172 responses but different sections of the survey were completed by each school. Consequently, there were 33,261 responses from schools that completed all components of the survey relevant to the current analysis. From these respondents, we removed all records with missing values (843; 2.53%). The retained analytical sample included 32,418 records. Given the small percentage of missing values, it is unreasonable that omitting these records have skewed the results.

Study measures

The primary outcomes were daily servings of energy drinks and shots (referred to as energy drinks herein) and sodas (diet, regular); and estimated daily sugar (g/day) and caffeine (mg/day) consumption based on self-reported intake of 12-ounce units (1 serving) of energy drinks, energy shots, sugar-sweetened sodas and diet sodas. Daily beverage intake was collected as part of the Monitoring the Future study and descriptions of each type of drink with examples were provided (S1 Table). Responses ranged from daily intake of "None", "Less than one", "One", "Two", "Three", "Four", "Five or six" or "Seven or more".

Estimated sugar (g/day) and caffeine (mg/day) intakes were calculated based on the reported number of servings and estimated average amount of either sugar or caffeine contained in each beverage type. These estimates were based on national consumer studies information [40, 41] which included data for 354 energy drinks, 87 energy shots, 83 soda drinks and 38 diet soda drinks. Using this calculation, the estimated average sugar (g) per unit was 24.91g/unit of energy drink, 1.83g/unit energy shot, 34.97g/unit of sugar-sweetened sodas and 0g/unit of diet soda. The average caffeine content per drink was estimated to be 110.05 mg/unit of energy drinks and 150.29 mg/unit of energy shots according to reported caffeine content for 20 popular energy drinks and 7 energy shots [40]. The average caffeine intake for both regular sodas and diet sodas was estimated to be 42.97 mg/unit based on reported caffeine content of 72 common soda drinks [40].

In addition to daily intake as a continuous variable, we evaluated if recommended sugar and/or caffeine intake were likely to be exceeded as a result of reported beverage intake alone. Assessing the likelihood of failing recommendations was of interest to further understand the impact of beverage consumption on sugar and caffeine intakes among adolescents. The WHO strongly recommends that energy intake from sugar should not exceed 10% of total energy intake and also suggests a conditional limit of 5% daily energy intake from added sugars [21]. We estimated the individual energy requirement for each participant using US Department of Agriculture (USDA) daily calorie needs for moderately active youth, stratified by age and sex [14], to determine if added sugar from the beverages studied exceeded 5% and 10% of estimated daily energy intake (S2 Table). As the recommended caffeine intake for adolescents is based on body weight and we did not have exact weights of participants, we extracted the weight at the 50th percentile according to each sex and age group (13–14 years in 8th grade; 15–16 years in 10th grade) from the Center for Disease Control (CDC) growth charts [42]. This weight was multiplied by the recommended daily caffeine intake for adolescents (≤2.5 mg/kg) [43] to generate the caffeine threshold for each age and sex.

Electronic device use was based on self-report responses of the number of hours per week of 1) computer use for school, 2) videogames use, 3) social media use, 4) watching TV (weighted average of hours per weekday and weekend day), and 5) talking on cellphone (no texting, social media use, etc.). The questionnaires included a range of responses (“None," “less than 1 hour,” “1–2 hours,” “3–5 hours,” “6–9 hours,” “10–19 hours,” 20–29 hours,” “30–39 hours,” or "40 or more" per week), which were transformed by using the mid-point for closed ranges, or the lowest value for open ranges. Weekly values were converted to daily values through division by seven.

Covariates included in the analysis of the relationship between sugar or caffeine consumption and electronic device use were: year of survey administration, grade (8th grade = 0), sex (female = 0), self-reported parental education as an indicator of socioeconomic status and hours spent unattended at home, a reported risk factor for unhealthy and risky behaviours [44]. Parental education level responses were “Completed grade school or less”, “Some high school”, “Completed high school”, “Some college”, “Completed college”, “Graduate or professional school after college”. Responses were transformed into a scale from 1–6 respectively. Survey questions with variable names, respective anchors and methods of transformation are included in S1 Table.

Statistical analysis

All analyses were performed in SPSS 25. Differences in the proportions of youth exceeding recommendations between 2013 and 2016 were analyzed using chi-square testing. All models were estimated with bootstrapping with 2,000 re-samples for generating bias-corrected 95% confidence intervals for estimates to avoid distributional assumptions, improve robustness [45] and to allow the statistical comparison of regression coefficients [46]. Regression analyses were used to predict daily caffeine and sugar intakes from the examined drinks, with daily hours of electronic device use and the aforementioned covariates as predictors. Logistic regression models were used to determine the relationship of hours of daily electronic device use with whether or not the child exceeded recommended sugar and caffeine intake from soda and ED, after controlling for covariates. For the odds ratios (OR), non-overlapping confidence intervals or distances from upper/lower bound to the estimate point that had <50% overlap was deemed indicative of significant (p<0.05) differences between the coefficients [46].

Results

Sample characteristics and beverage intake trends

The analytical sample (n = 32,418) included 15,834 students in grade 8 (48.8%) and 16,584 in grade 10 (51.2%) who responded to all questions regarding daily electronic device use and soda and energy drink consumption. Participants were nearly equal male (48.1%; n = 15,593) and female (Table 1) and 13.9% were Black; 64.8% White and 21.3% Hispanic.

Tab. 1. Sample population from each survey year (total n = 32,418), and home characteristics.
Sample population from each survey year (total n = 32,418), and home characteristics.

Reported consumption of energy drinks, sodas and beverage-related sugar and caffeine intake declined each year from 2013 to 2016 (Figs 13; Table 2). Accordingly, the number of youth who exceeded conditional (5% of energy intake) and strong (10% of energy intake) recommendations for sugar intake and recommendations for caffeine intake (<2.5mg/kg) as a result of energy drink and soda consumption also declined. In 2013, 53.8% of males and 45.5% of females exceeded the conditional sugar intake recommendations and 32.2% of males and 25.7% of females exceeded the strong sugar intake recommendations. In 2016, the frequency of exceeding conditional recommendations decreased to 47.0% among males and 41.6% among females while 26.0% of males and 21.6% of females exceeded the strong recommendations. Declines were particularly notable for males in Grade 8. Girls in Grade 10 reported declines in the more stringent limit of added glucose intake only. In 2013, 26.0% of youth exceeded recommended caffeine intake from soda energy drink consumption compared to 21.2% in 2016. This decline was notable across sex and grade level, but was not significant in girls in Grade 10. Thus, the largest decline in exceeding recommended sugar and caffeine intake thresholds was seen in males in Grade 8 and these declines over time were less evident in the older girls.

Average daily intake of energy drinks and soda (servings per day) by year and sex.
Fig. 1. Average daily intake of energy drinks and soda (servings per day) by year and sex.
Daily sugar intake attributable to consumption of energy drinks and sodas among males and females from 2013–2016.
Fig. 2. Daily sugar intake attributable to consumption of energy drinks and sodas among males and females from 2013–2016.
Daily caffeine intake attributable to consumption of energy drinks and sodas among males and females from 2013–2016.
Fig. 3. Daily caffeine intake attributable to consumption of energy drinks and sodas among males and females from 2013–2016.
Tab. 2. Frequency of youth who exceed recommended sugar and caffeine intakes from energy drinks and sodas during 2013–2016 (n = 32,418) by sex, grade and year.
Frequency of youth who exceed recommended sugar and caffeine intakes from energy drinks and sodas during 2013–2016 (n = 32,418) by sex, grade and year.

Electronic device use and estimated daily sugar and caffeine intake from sodas and ED

On average, these youth used their devices as follows; 1.97 h/day (SD = 1.36) on TV, 1.48 h/day (SD = 1.82) on video games, 1.36 h/day (SD = 1.74) on social media, 0.59 h/day (SD = 1.26) talking on the cellphone and 0.58 h/day (SD = 1.01) on computer use for school (Fig 4). Reported use of electronic devices was higher among females compared to males throughout the study duration.

Reported electronic device use (hours per day) among males and females from 2013–2016*.
Fig. 4. Reported electronic device use (hours per day) among males and females from 2013–2016*.
* Note that multi-tasking is possible (i.e. use more than one device at once) and therefore cumulative hours of electronic device use should be interpreted with caution.

Reported use of all electronic devices except ‘computer use for school’ was related to higher estimated daily sugar and caffeine intake, independent of grade and sex of the participant, parental education and hours spent unattended at home. Daily sugar intake was 6.92g (6.31, 7.48; p< 0.001) higher for each hour spent watching TV, 5.56g (4.68, 6.50; p< 0.001) higher per hour talking on the phone, 1.99g (1.54, 2.46; p< 0.001) higher per hour spent playing video-games and 1.65g (1.13, 2.14; p< 0.001) higher for each additional hour of social-media use (Table 3). When considering the daily use of each electronic device and the associated increase in sugar consumption per hour of device use, TV use was associated with an additional 14g of sugar per day, while videogame use, talking on a cellphone and social media use were, together, associated with an additional 8.2g of added sugar intake each day.

Tab. 3. Regression models of caffeine and sugar intakes and associations with electronic device use.
Regression models of caffeine and sugar intakes and associations with electronic device use.

Caffeine intake from beverages was also higher with increasing electronic device use. Caffeine intake was 7.14mg (3.65, 11.21 p< 0.001) higher/ hour of computer use for school, 2.78mg (1.09, 4.40; p< 0.001) higher/hour of video games, 5.21mg (3.51, 6.99; p< 0.001) higher/hour of social media use, 16.92mg (14.65, 19.03; p< 0.001) higher/hour watching television, and 21.86mg (18.25, 25.81; p< 0.001) higher/hour talking on the phone. Considering device use and additional intake per hour of use, TV watching was associated with intake of 32 mg/day caffeine while video gaming was associated with estimated intake of only 4.4 additional mg caffeine/day.

Use of electronic devices and the likelihood of exceeding recommended added sugar and caffeine intakes

Sugar intake

Grade level, parental education and sex were independently related to the risk of exceeding added sugar intake recommendations. Similar associations were found for both the strong (10%) sugar intake recommendation (Table 4) and the conditional (5%) sugar recommendation (Table 5), however the strong recommendation demonstrated more significant associations with screen use. The likelihood of exceeding the strong (10%) sugar intake recommendations (Table 4) was seven percent higher with each hour of social media and video game use (1.05–1.09; p<0.001), 32 percent higher per hour of TV watching (1.29–1.35; p<0.001) and 15% higher for each hour spent talking on the cellphone (1.14–1.20; p<0.001). Considering that students spent most of their electronic device use time watching TV and that each hour of TV was associated with the highest likelihood of exceeding sugar recommendations, it is evident that time spent watching TV had the greatest influence on the likelihood of exceeding the recommendations for added sugar intake from the beverages studied.

Tab. 4. Logistic regression models of the relationship of electronic device use to the likelihood of exceeding strong (10%) sugar intake recommendations through beverage consumption.
Logistic regression models of the relationship of electronic device use to the likelihood of exceeding strong (10%) sugar intake recommendations through beverage consumption.
Tab. 5. Logistic regression models of the relationship of electronic device use and the likelihood of exceeding conditional (5%) sugar intake recommendations through beverage consumption.
Logistic regression models of the relationship of electronic device use and the likelihood of exceeding conditional (5%) sugar intake recommendations through beverage consumption.

Caffeine intake

Daily usage times of all electronic devices except “computer use for school”, were also independently associated with increased odds for exceeding recommended daily caffeine intake from sodas and ED (Table 6). Independent of the covariates, the odds of exceeding caffeine recommendations were higher by 28% (OR = 1.25–1.31; p<0.001) per hour of television watching, 18% (OR = 1.15–1.21; p<0.001) per hour of talking on a cellphone, 9% (1.06–1.11; p<0.001) per hour of social media use, and 4% (1.02–1.06; p<0.001) per hour of video gaming.

Tab. 6. Logistic regression models assessing the OR of exceeding caffeine intake recommendations (>2.5mg/kg) with covariates and reported electronic device uses.
Logistic regression models assessing the OR of exceeding caffeine intake recommendations (>2.5mg/kg) with covariates and reported electronic device uses.

Discussion

The observed decline in soda and energy drink consumption amongst youth from 2013 to 2016 observed in this study is consistent with findings from other large cohort studies [18] [19] [20]. This may be the result of government initiatives restricting the sale of SSBs and prohibiting the sale of energy drinks in schools following a statement from the AAP in 2004 [47]. However, energy intake [19] and caffeine intake [20] from these beverage sources continues to be high, with close to 50% of youth exceeding 5% of energy intake beverage-sourced sugars and 22% to 26% of females and males exceeding 10% energy from sugars.

Our primary objective was to examine the relationship of electronic device use and consumption of sugar and caffeine from soda and ED. We identified a direct relationship between sugar and caffeine intake from beverages and electronic device use. Our findings suggest that watching TV and talking on the cellphone were associated with the greatest odds of exceeding recommended intakes of sugar and caffeine. However, in contrast to our expectation, time spent video gaming contributed relatively little to added sugar and caffeine intake.

In previous studies, time spent watching TV was independently associated with SSB consumption among adolescents [32]. Additionally, in a large cross-sectional survey of grade 9–12 students in the US conducted in 2009, watching >2 hours of TV per day significantly increased the odds of consuming ≥1 energy drink and ≥1 soda per day [35]. Thus, despite the rapidly changing use patterns of electronic devices, watching TV remains an important correlate of SSB consumption. The strength of this relationship may in part due to advertising of beverages to youth [48, 49] or because both hands are free while watching TV enabling intake of food and beverages [33, 50], which may lead to poorer diet quality [51] compared to another electronic device use such as video gaming or computer use.

Few studies have reported on the relationship between SSB or energy drinks and time spent on a cellphone. One previous study has described a positive relationship between cellphone use and energy intake. However this study did not investigate talking on the phone exclusively [52]. Given that many adolescents own cellphones, further research could be conducted to better understand this relationship between cellphone use (i.e. talking) and diet quality.

The weak relationship between video game use and both sugar and caffeine intakes in the current study was surprising and counter to our original hypothesis. An increase of one hour spent playing video games increased the odds of exceeding sugar and caffeine intakes by only approximately 7%. Previous studies have demonstrated a much stronger relationship among energy drink consumption and video-gaming. Larson et al. [53] found that, among grade 6–12 students, consuming at least one energy drink per week was associated with an additional four hours of video-gaming per week [53]. Similarly, a large survey of 14–18-year-old youth in 2008 demonstrated that males and females who reported playing video games on a weekly basis were more likely to consume between one and three energy drinks per day [31]. While consumption of SSB including energy drinks has been consistently associated with greater video game use among adolescents and children [30, 33, 35, 54, 55], previous studies often do not differentiate energy drinks from other SSB beverages such as sodas, limiting our ability to compare results. [56]

Each hour of social media use is related to the risk of exceeding both caffeine and added sugar recommendations. Social media use was higher in females than males, but overall, contributed less to electronic device use time than either video games or watching TV. Although research involving social media use and beverage intake is not well-described, a previous study by Sampasa-Kayinga et al. (2015) collected social network use from nearly 10,000 adolescent students (grades 7–12) in Canada as part of a drug use and health survey. The findings demonstrated that nearly 40% of youth spent on average of 1–2 hours of social media use per day, similar to the average 1.36 daily hours reported here. When the relationship between beverage intake and social networking site use was investigated, youth who self-reported using social networking sites for two hours per day were nearly twice as likely to consume SSB in the past week and were more than three times more likely to drink an energy drink in the past year after adjusting for similar covariates (age, sex, socioeconomic status) [31, 57]. Although the average time spent on social media was similar to the current study, the relationship between energy drink and soda consumption is difficult to compare given the different time frame referred to in this previous study. However, an increased risk of consuming caffeine and sugar from beverage sources including SSB and energy drinks is evident in both studies. Given that previous research has limited focus on the relationship of social media use and nutritional behaviours, this may also highlight an area where further research is needed.

In summary, the current results demonstrate a direct relationship between use of various electronic devices and sugar and caffeine intake from beverages. These findings are important to consider given poorer diet quality and increased energy intake has also been correlated with screen time [26, 58], thought to be due in part to distracted eating and snacking [18]. A higher energy intake contributed to by added sugars found in SSB may drive weight gain [1, 2] and associated co-morbidities [59] while reducing sugar intake may help to improve weight status [6062], diet quality [63, 64] and metabolic health[65] [66, 67]. Additionally, high reported levels of screen time in this population is also concerning given that time spent on electronic devices is reported to promote a sedentary lifestyle [33] and lower physical activity, increased anxiety and depression [68] and may also impact development in young children [69].

Identifying which types of electronic use are most strongly correlated with sugar and caffeine intake and their appropriate use among adolescents is important to help inform public health efforts to improve lifestyles among this demographic. [56]Furthermore, previous studies have shown that dietary habits are worsened when sleep quality is affected [70] potentially due to disinhibited or distracted eating and consumption of high energy foods, which has been shown to increase the risk of obesity in adults [26, 71] and children [72]. While the current findings did not assess sleep, the effect of lack of sleep on soda and energy drink consumption and electronic device use highlights a potential opportunity for further research.—especially given that energy drinks may induce increased wakefulness [16] and sleep duration is inversely related to soda consumption[73] and screen time [10, 74, 75].

The findings from this large, nationally-representative population of youth sampled from all US states and provides an increased understanding of the contribution of energy drinks and sodas relative to current recommendations. Additionally, the multiple electronic device uses reported allow for a more nuanced understanding and comparison of the relationship of technology use with sugar and caffeine intake. Further, the inclusion of multiple years has allowed study of recent trends in these two important youth behaviour areas. However, there are limitations that should be considered. The sources of the caffeine and sugar intakes were limited to soda and energy drinks and the content per drink was estimated based on average content amongst the beverage groups; and were not based on intake of specific brands. Although older students had lower caffeine intake from soda and ED, the report that more than 20% of youth exceeded recommended caffeine intake from soda and energy drink consumption may be an underestimate of overall caffeine intake as other sources of caffeine such as coffee or tea were not included [76]. Similarly, we estimated the daily energy intake based on the 50th percentile weight as reported by the CDC; we were unable to apply a specific intake for each person based on their individual body weights. Thus, we have only been able to estimate the energy intake needs, and consequently the 5% and 10% threshold for added sugar that are recommended by the WHO. Multi-tasking was not directly assessed in the survey and the cumulative hours of electronic device use should be interpreted with caution, as there is the possibility that youth utilized more than one electronic device at a time. Similarly, TV use was described as total time spent watching TV, however youth may not have included TV time on multiple different mediums (e.g. laptop and online streaming). Finally, the results are based on self-reports and may be subject to recall bias. Differences between children in Grade 8 and Grade 10 from 2013–2016 were obtained comparing cross-sectional data and do not represent a decline with increasing age of the individual. Such within-individual trends cannot be assessed with our data.

Conclusion

To conclude, a trend of reduced reported consumption of sodas and energy drinks amongst American youth from 2013–2016 was identified while electronic device use remained relatively stable. In spite of these declines, over 27% of youth exceeded strong (<10%) sugar intake recommendations and 21% exceeded recommended caffeine intakes based on soda and energy drink consumption alone in 2016. Consumption of added sugar and caffeine and risks of exceeding respective thresholds through energy drink and soda intake were higher amongst youth with greater daily usage of TV, video games, talking on the phone and social media use. Considering these associations may assist pediatricians, public health advocates and parents to develop strategies to reduce the risk of excessive intakes of added sugar and caffeine amongst youth.

Supporting information

S1 Table [docx]
Variables, respective measures and transformations used for analysis.

S2 Table [docx]
Estimated weights and respective caffeine and sugar intake according to age and sex.


Zdroje

1. Malik VS, Pan A, Willett WC, Hu FB. Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta-analysis. Am J Clin Nutr. 2013;98(4):1084–102. doi: 10.3945/ajcn.113.058362 23966427

2. Luger M, Lafontan M, Bes-Rastrollo M, Winzer E, Yumuk V, Farpour-Lambert N. Sugar-Sweetened Beverages and Weight Gain in Children and Adults: A Systematic Review from 2013 to 2015 and a Comparison with Previous Studies. Obes Facts. 2017;10(6):674–93. doi: 10.1159/000484566 29237159

3. Williams RD Jr., Housman JM, Odum M, Rivera AE. Energy Drink Use Linked to High-sugar Beverage Intake and BMI among Teens. Am J Health Behav. 2017;41(3):259–65. doi: 10.5993/AJHB.41.3.5 28376970

4. Rodriguez LA, Madsen KA, Cotterman C, Lustig RH. Added sugar intake and metabolic syndrome in US adolescents: cross-sectional analysis of the National Health and Nutrition Examination Survey 2005–2012. Public Health Nutr. 2016;19(13):2424–34. doi: 10.1017/S1368980016000057 26932353

5. Loh DA, Moy FM, Zaharan NL, Jalaludin MY, Mohamed Z. Sugar-sweetened beverage intake and its associations with cardiometabolic risks among adolescents. Pediatr Obes. 2017;12(1):e1–e5. doi: 10.1111/ijpo.12108 26843446

6. Vos MB, Kaar JL, Welsh JA, Van Horn LV, Feig DI, Anderson CAM, et al. Added Sugars and Cardiovascular Disease Risk in Children: A Scientific Statement From the American Heart Association. Circulation. 2016;135(19). doi: 10.1161/CIR.0000000000000439 27550974

7. Malik VS. Sugar sweetened beverages and cardiometabolic health. Curr Opin Cardiol. 2017;32(5):572–9. doi: 10.1097/HCO.0000000000000439 28639973

8. Sheiham A, James W. Diet and dental caries: the pivotal role of free sugars reemphasized. J Dent Res. 2015;94(10):1341–7. doi: 10.1177/0022034515590377 26261186

9. Calamaro CJ, Yang K, Ratcliffe S, Chasens ER. Wired at a Young Age: The Effect of Caffeine and Technology on Sleep Duration and Body Mass Index in School-Aged Children. J Pediatr Health Care. 2012;26(4):276–82. doi: 10.1016/j.pedhc.2010.12.002 22726712

10. Turel O, Romashkin A, Morrison K. A model linking video gaming, sleep quality, sweet drinks consumption and obesity among children and youth. Clin Obes. 2017;7(4):191–8. doi: 10.1111/cob.12191 28320073

11. Johnson RK, Lichtenstein AH, Anderson CAM, Carson JA, Despres JP, Hu FB, et al. Low-Calorie Sweetened Beverages and Cardiometabolic Health: A Science Advisory From the American Heart Association. Circulation. 2018;138(9):e126–e40. doi: 10.1161/CIR.0000000000000569 30354445

12. Swithers SE. Artificial sweeteners are not the answer to childhood obesity. Appetite. 2015;93:85–90. doi: 10.1016/j.appet.2015.03.027 25828597

13. Styne DM, Arslanian SA, Connor EL, Farooqi IS, Murad MH, Silverstein JH, et al. Pediatric obesity—assessment, treatment, and prevention: an Endocrine Society Clinical Practice guideline. J Clin Endocrinol Metab. 2017;102(3):709–57. doi: 10.1210/jc.2016-2573 28359099

14. United States Department of Agriculture. Dietary Guidelines for Americans. In: Department of Agriculture, editor. 8 ed: USDA; 2015. p. 144.

15. Pound CM, Blair B, Boctor DL, Casey LM, Critch JN, Farrell C, et al. Energy and sports drinks in children and adolescents. Paediatr Child Health. 2017;22(7):406–10. doi: 10.1093/pch/pxx132 29491725

16. Visram S, Cheetham M, Riby DM, Crossley SJ, Lake AA. Consumption of energy drinks by children and young people: a rapid review examining evidence of physical effects and consumer attitudes. BMJ Open. 2016;6(10):e010380. doi: 10.1136/bmjopen-2015-010380 27855083

17. Sports drinks and energy drinks for children and adolescents: are they appropriate? Pediatrics. 2011;127(6):1182–9. doi: 10.1542/peds.2011-0965 21624882

18. Kit BK, Fakhouri TH, Park S, Nielsen SJ, Ogden CL. Trends in sugar-sweetened beverage consumption among youth and adults in the United States: 1999–2010. Am J Clin Nutr. 2013;98(1):180–8. doi: 10.3945/ajcn.112.057943 23676424

19. Bleich SN, Vercammen KA, Koma JW, Li Z. Trends in Beverage Consumption Among Children and Adults, 2003–2014. Obesity (Silver Spring). 2018;26(2):432–41. doi: 10.1002/oby.22056 29134763

20. Verster JC, Koenig J. Caffeine intake and its sources: A review of national representative studies. Crit Rev Food Sci Nutr. 2018;58(8):1250–9. doi: 10.1080/10408398.2016.1247252 28605236

21. World Health Organization. Guideline: sugars intake for adults and children. Geneva, Switzerland: World Health Organization; 2015. 49 p.

22. WHO calls on countries to reduce sugars intake among adults and children [Internet]. Geneva, Switzerland: WHO; 2015

23. Tajeu GS, Sen B. New pathways from short sleep to obesity? Associations between short sleep and “secondary” eating and drinking behavior. Am J Health Promot. 2017;31(3):181–8. doi: 10.4278/ajhp.140509-QUAN-198 26559711

24. Costa BM, Hayley A, Miller P. Young adolescents’ perceptions, patterns, and contexts of energy drink use. A focus group study. Appetite. 2014;80:183–9. doi: 10.1016/j.appet.2014.05.013 24852220

25. Visram S, Crossley SJ, Cheetham M, Lake A. Children and young people’s perceptions of energy drinks: A qualitative study. PloS one. 2017;12(11):e0188668. doi: 10.1371/journal.pone.0188668 29190753

26. Chaput JP, Klingenberg L, Astrup A, Sjödin AM. Modern sedentary activities promote overconsumption of food in our current obesogenic environment. Obes Rev. 2011;12(5):e12–e20. doi: 10.1111/j.1467-789X.2010.00772.x 20576006

27. Ogden J, Coop N, Cousins C, Crump R, Field L, Hughes S, et al. Distraction, the desire to eat and food intake. Towards an expanded model of mindless eating. Appetite. 2013;62:119–26. doi: 10.1016/j.appet.2012.11.023 23219989

28. Chaput J-P, Visby T, Nyby S, Klingenberg L, Gregersen NT, Tremblay A, et al. Video game playing increases food intake in adolescents: a randomized crossover study. Am J Clin Nutr. 2011;93(6):1196–203. doi: 10.3945/ajcn.110.008680 21490141

29. Mazarello Paes V, Hesketh K, O'Malley C, Moore H, Summerbell C, Griffin S, et al. Determinants of sugar‐sweetened beverage consumption in young children: a systematic review. Obes Rev. 2015;16(11):903–13. doi: 10.1111/obr.12310 26252417

30. Ranjit N, Evans MH, Byrd-Williams C, Evans AE, Hoelscher DM. Dietary and activity correlates of sugar-sweetened beverage consumption among adolescents. Pediatrics. 2010;126(4):e754–61. doi: 10.1542/peds.2010-1229 20876172

31. Sampasa-Kanyinga H, Chaput J-P. Consumption of sugar-sweetened beverages and energy drinks and adherence to physical activity and screen time recommendations among adolescents. Int J Adolesc Med Health. 2017;29(5). doi: 10.1515/ijamh-2015-0098 26926857

32. Gebremariam M, Altenburg T, Lakerveld J, Andersen L, Stronks K, Chinapaw M, et al. Associations between socioeconomic position and correlates of sedentary behaviour among youth: a systematic review. Obes Rev. 2015;16(11):988–1000. doi: 10.1111/obr.12314 26317685

33. Kenney EL, Gortmaker SL. United States Adolescents' Television, Computer, Videogame, Smartphone, and Tablet Use: Associations with Sugary Drinks, Sleep, Physical Activity, and Obesity. J Pediatr. 2017;182:144–9. doi: 10.1016/j.jpeds.2016.11.015 27988020

34. Scully M, Morley B, Niven P, Crawford D, Pratt IS, Wakefield M. Factors associated with high consumption of soft drinks among Australian secondary-school students. Public Health Nutr. 2017;20(13):2340–8. doi: 10.1017/S1368980017000118 28238298

35. Park S, Blanck HM, Sherry B, Brener N, O’Toole T. Factors associated with sugar-sweetened beverage intake among United States high school students. J Nutr. 2012;142(2):306–12. doi: 10.3945/jn.111.148536 22223568

36. Marsh S, Mhurchu CN, Maddison R. The non-advertising effects of screen-based sedentary activities on acute eating behaviours in children, adolescents, and young adults. A systematic review. Appetite. 2013;71:259–73. doi: 10.1016/j.appet.2013.08.017 24001394

37. Johnston L, O'Malley P, Bachman J, Schulenberg J. Monitoring the Future: National Results on Drug Use 2012: Overview of Key Findings on Adolescent Drug Use. University of Michigan. Ann Arbor: Institute for Social Research, The University of Michigan. 2013.

38. Miech RA, Johnston LD, Bachman JG, O'Malley PM, Schulenberg JE. Monitoring the Future: A Continuing Study of American Youth (12th-Grade Survey). Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]; 2018.

39. Bachman JG, Johnston LD, O’Malley PM, Schulenberg JE. The Monitoring the Future project after thirty-seven years: design and procedures (Monitoring the Future Occasional Paper No. 76). Ann Arbor, MI: Institute for Social Research. 2011:93.

40. Sugar in Drinks [Web Page]. Caffeine Informer; 2018 [updated 2018; cited 2018 June 2018]. Available from: https://www.caffeineinformer.com/sugar-in-drinks.

41. Caffeine Content of Drinks [Web Page]. Online: Caffeine Informer; 2018 [updated 2018; cited 2018 June 2018]. Available from: https://www.caffeineinformer.com/the-caffeine-database.

42. Kuczmarski RJ. 2000 CDC growth charts for the United States; methods and development. Vital Health Stat. 2002;11(246):1–190.

43. Wikoff D, Welsh BT, Henderson R, Brorby GP, Britt J, Myers E, et al. Systematic review of the potential adverse effects of caffeine consumption in healthy adults, pregnant women, adolescents, and children. Food Chem Toxicol. 2017;109:585–648. doi: 10.1016/j.fct.2017.04.002 28438661

44. Miles G, Siega-Riz AM. Trends in food and beverage consumption among infants and toddlers: 2005–2012. Pediatrics. 2017;139(6):e20163290. doi: 10.1542/peds.2016-3290 28562265

45. Mooney CZ, Duval RD, Duvall R. Quantitative applications in the social sciences. Thousand Oaks, CA: Sage; 1993.

46. Cumming G. Inference by eye: reading the overlap of independent confidence intervals. Stat Med. 2009;28(2):205–20. doi: 10.1002/sim.3471 18991332

47. Murray R, Bhatia J, Okamoto J, Allison M, Ancona R, Attisha E, et al. Snacks, sweetened beverages, added sugars, and schools. Pediatrics. 2015;135(3):575–83. doi: 10.1542/peds.2014-3902 25713277

48. Kumar G, Onufrak S, Zytnick D, Kingsley B, Park S. Self-reported advertising exposure to sugar-sweetened beverages among US youth. Public Health Nutr. 2015;18(7):1173–9. doi: 10.1017/S1368980014001785 25166512

49. Emond JA, Sargent JD, Gilbert-Diamond D. Patterns of Energy Drink Advertising Over US Television Networks. J Nutr Educ Behav. 2015;47(2):120–6.e1. doi: 10.1016/j.jneb.2014.11.005 25754297

50. Lee SC, Koleilat M, Hernandez LM, Whaley SE, Davis JN. Screen Time Associated to Unhealthy Diets in Low-Income Children. J Food Nutr Res (Newark). 2016;4(2):94–9. doi: 10.12691/jfnr-4-2-5

51. Ciccone J, Woodruff SJ, Fryer K, Campbell T, Cole M. Associations among evening snacking, screen time, weight status, and overall diet quality in young adolescents. Appl Physiol Nutr Metab. 2013;38(7):789–94. doi: 10.1139/apnm-2012-0374 23980738

52. da Mata Gonçalves RF, de Almeida Barreto D, Monteiro PI, Zangeronimo MG, Castelo PM, van der Bilt A, et al. Smartphone use while eating increases caloric ingestion. Physiol Behav. 2019;204:93–9. doi: 10.1016/j.physbeh.2019.02.021 30776379

53. Larson N, DeWolfe J, Story M, Neumark-Sztainer D. Adolescent Consumption of Sports and Energy Drinks: Linkages to Higher Physical Activity, Unhealthy Beverage Patterns, Cigarette Smoking, and Screen Media Use. J Nutr Educ Behav. 2014;46(3):181–7. doi: 10.1016/j.jneb.2014.02.008 24809865

54. Desai RA, Krishnan-Sarin S, Cavallo D, Potenza MN. Video-gaming among high school students: health correlates, gender differences, and problematic gaming. Pediatrics. 2010;126(6):e1414–e24. doi: 10.1542/peds.2009-2706 21078729

55. Al-Hazzaa HM, Al-Sobayel HI, Abahussain NA, Qahwaji DM, Alahmadi MA, Musaiger AO. Association of dietary habits with levels of physical activity and screen time among adolescents living in Saudi Arabia. J Hum Nutr Diet. 2014;27:204–13. doi: 10.1111/jhn.12147 23889093

56. Lyons EJ, Tate DF, Ward DS, Wang X. Energy intake and expenditure during sedentary screen time and motion-controlled video gaming. Am J Clin Nutr. 2012;96(2):234–9. doi: 10.3945/ajcn.111.028423 22760571

57. Sampasa-Kanyinga H, Chaput J-P, Hamilton HA. Associations between the use of social networking sites and unhealthy eating behaviours and excess body weight in adolescents. Br J Nutr. 2015;114(11):1941–7. doi: 10.1017/S0007114515003566 26400488

58. Avery A, Anderson C, McCullough F. Associations between children's diet quality and watching television during meal or snack consumption: A systematic review. Matern Child Nutr. 2017;13(4):e12428. doi: 10.1111/mcn.12428 28211230

59. Scharf RJ, DeBoer MD. Sugar-sweetened beverages and children's health. Annu Rev Public Health. 2016;37:273–93. doi: 10.1146/annurev-publhealth-032315-021528 26989829

60. Avery A, Bostock L, McCullough F. A systematic review investigating interventions that can help reduce consumption of sugar-sweetened beverages in children leading to changes in body fatness. J Hum Nutr Diet. 2015;28 Suppl 1:52–64. doi: 10.1111/jhn.12267 25233843; PubMed Central PMCID: PMC4309175.

61. Lustig RH, Mulligan K, Noworolski SM, Tai VW, Wen MJ, Erkin‐Cakmak A, et al. Isocaloric fructose restriction and metabolic improvement in children with obesity and metabolic syndrome. Obesity. 2016;24(2):453–60. doi: 10.1002/oby.21371 26499447

62. de Ruyter JC, Olthof MR, Seidell JC, Katan MB. A trial of sugar-free or sugar-sweetened beverages and body weight in children. N Engl J Med. 2012;367(15):1397–406. doi: 10.1056/NEJMoa1203034 22998340.

63. Hedrick VE, Davy BM, Porter KJ, Zoellner JM, Wen Y, Estabrooks PA. Dietary quality changes in response to a sugar-sweetened beverage-reduction intervention: results from the Talking Health randomized controlled clinical trial. Am J Clin Nutr. 2017;105(4):824–33. doi: 10.3945/ajcn.116.144543 28251935

64. Leung CW, DiMatteo SG, Gosliner WA, Ritchie LD. Sugar-Sweetened Beverage and Water Intake in Relation to Diet Quality in U.S. Children. Am J Prev Med. 2018;54(3):394–402. doi: 10.1016/j.amepre.2017.11.005 29338950

65. Lee KW, Shin D. A Healthy Beverage Consumption Pattern Is Inversely Associated with the Risk of Obesity and Metabolic Abnormalities in Korean Adults. J Med Food. 2018;21(9):935–45. doi: 10.1089/jmf.2017.0119 29569988.

66. Schwimmer JB, Ugalde-Nicalo P, Welsh JA, Cordero M, Harlow KE, Alazraki A, et al. Effect of a Low Free Sugar Diet vs Usual Diet on Nonalcoholic Fatty Liver Disease in Adolescent Boys: A Randomized Clinical Trial. JAMA. 2019;321(3):256–65. doi: 10.1001/jama.2018.20579 30667502

67. Duffey KJ, Davy BM. The healthy beverage index is associated with reduced cardiometabolic risk in US adults: A preliminary analysis. J Acad Nutr Diet. 2015;115(10):1682–9. e2. doi: 10.1016/j.jand.2015.05.005 26184445

68. Gunnell KE, Flament MF, Buchholz A, Henderson KA, Obeid N, Schubert N, et al. Examining the bidirectional relationship between physical activity, screen time, and symptoms of anxiety and depression over time during adolescence. Prev Med. 2016;88:147–52. doi: 10.1016/j.ypmed.2016.04.002 27090920

69. Madigan S, Browne D, Racine N, Mori C, Tough S. Association Between Screen Time and Children’s Performance on a Developmental Screening Test. JAMA Pediatr. 2019;173(3):244–50. doi: 10.1001/jamapediatrics.2018.5056 30688984

70. Chaput J-P. Sleep patterns, diet quality and energy balance. Physiol Behav. 2014;134:86–91. doi: 10.1016/j.physbeh.2013.09.006 24051052

71. Chaput J-P, Després J-P, Bouchard C, Tremblay A. The association between short sleep duration and weight gain is dependent on disinhibited eating behavior in adults. Sleep. 2011;34(10):1291–7. doi: 10.5665/SLEEP.1264 21966060

72. Robinson TN, Banda JA, Hale L, Lu AS, Fleming-Milici F, Calvert SL, et al. Screen media exposure and obesity in children and adolescents. Pediatrics. 2017;140(Supplement 2):S97–S101.

73. Chaput J-P, Tremblay MS, Katzmarzyk PT, Fogelholm M, Hu G, Maher C, et al. Sleep patterns and sugar-sweetened beverage consumption among children from around the world. Public Health Nutr. 2018;21(13):2385–93. doi: 10.1017/S1368980018000976 29681250

74. LeBourgeois MK, Hale L, Chang A-M, Akacem LD, Montgomery-Downs HE, Buxton OM. Digital media and sleep in childhood and adolescence. Pediatrics. 2017;140(Supplement 2):S92–S6.

75. Chaput J-P, Leduc G, Boyer C, Bélanger P, LeBlanc AG, Borghese MM, et al. Electronic screens in children’s bedrooms and adiposity, physical activity and sleep: do the number and type of electronic devices matter? Can J Public Health. 2014;105(4):e273–e9. 25166130

76. Drewnowski A, Rehm CD. Sources of Caffeine in Diets of US Children and Adults: Trends by Beverage Type and Purchase Location. Nutrients. 2016;8(3):154. Epub 2016/03/16. doi: 10.3390/nu8030154 26978391


Článek vyšel v časopise

PLOS One


2019 Číslo 10