Association between screen time and obesity in US adolescents: A cross-sectional analysis using National Survey of Children’s Health 2016–2017

Chighaf Bakour; Fahad Mansuri; Courtney Johns-Rejano; Michelle Crozier; Ronee Wilson; William Sappenfield

doi:10.1371/journal.pone.0278490

. 2022 Dec 1;17(12):e0278490. doi: 10.1371/journal.pone.0278490

Association between screen time and obesity in US adolescents: A cross-sectional analysis using National Survey of Children’s Health 2016–2017

Chighaf Bakour ^1,^2,^*, Fahad Mansuri ¹, Courtney Johns-Rejano ³, Michelle Crozier ^1,⁴, Ronee Wilson ^1,², William Sappenfield ^1,²

Editor: Yih-Kuen Jan⁵

PMCID: PMC9714705 PMID: 36454793

Abstract

Objective

This study examines the association between time spent watching TV, playing video games, using a computer or handheld device (screen time), and BMI among U.S. adolescents, and potential effect modification of these associations by sex, sleep duration, and physical activity.

Methods

A secondary analysis of 10–17-year-old participants in the 2016–2017 National Survey of Children’s Health was conducted. Multivariable logistic regression was used to examine the association between parent-reported screen time and BMI categories and effect modification by sex, sleep duration and physical activity.

Results

The analysis included 29,480 adolescents (49.4% female). Those with ≥1 hour (vs <1 hour) of TV/video games per day were more likely to be overweight/obese (adjusted Odds Ratio (aOR) 1–3 hours = 1.4; 95% Confidence Interval (CI)1.19, 1.65; aOR ≥4 hours = 2.19; 95% CI 1.73, 2.77). This association was stronger in adolescents who did not meet the guidelines for physical activity (aOR ≥ 4 hours = 3.04; 95% CI: 2.1, 4.4) compared with those who did (aOR ≥ 4 hours = 1.64; 95% CI: 0.72, 3.72). Using computers/handheld devices was associated with a smaller increase in odds of overweight/obesity (aOR ≥4 hours = 1.53; 95% CI:1.19, 1.97).

Conclusion

Watching TV or playing video games for ≥1 hour per day is associated with obesity in adolescents who did not meet the guidelines for physical activity. Using computers or handheld devices seems to have a weaker association with BMI compared with TV/video games.

Introduction

Childhood and adolescent obesity is a global public health crisis with one in five youth estimated to be overweight or obese [1]. Childhood obesity is a serious problem in the US, putting children and adolescents at risk for poor health [2]. In the U.S. between 2017 and 2020, the prevalence of obesity amongst children aged 6- to 11- years and adolescents aged 12- to 19- years was 20.7%, and 22.2%, respectively [3]. Obesity in childhood and adolescence increases the risk of many outcomes including obesity, hypertension, diabetes, asthma, sleep apnea, and dyslipidemia in adulthood [2, 4, 5]. Thus, it is paramount to identify emerging risk factors of obesity in adolescents that may be targeted with early interventions to prevent negative health outcomes in adulthood [4, 5].

Lack of physical activity, poor sleeping and eating habits, consumption of sugary beverages, and a sedentary lifestyle are important risk factors for overweight/obesity in adolescents [5–9]. Time spent in front of screens such as television, computer, or hand held devices has also been implicated as a risk factor for obesity, as it is commonly accompanied by poor eating habits, sedentary behavior, and lack of physical activity [10]. A recent study found that more than 65% of adolescents in the US exceed the recommended amount of screen time of no more than two hours of recreational screen time per day [11]. Screen time has steadily increased among adolescents, and the proportion of teens (ages 14–17) reporting being online almost constantly has doubled from 24% in 2015 to 45% in 2018 [12]. A meta-analysis of 52 studies (n = 44,707) found that TV viewing was positively associated with body fatness in children and adolescents [13], and another meta-analysis (16 studies, n = 17,920) published in 2019 found that screen time of two or more hours per day was associated with 67% (Odds Ratio (OR) = 1.67 95% Confidence Interval (CI): 1.48–1.88) higher odds of overweight/obesity in children and adolescents [10]. Similar positive associations between excessive screen time and overweight/obesity in adolescents have been reported by numerous cross-sectional [14–20] and longitudinal [21–23] studies in different ethnicities and populations. Thus, there is ample evidence showing that excessive screen time is positively associated with obesity/overweight in adolescents. However, some questions remain to be answered, especially regarding the difference in the association between males and females, which was examined in few studies with inconsistent findings [29, 31, 34, 35]. Additionally, the potential interaction between screen time and other risk factors for obesity, namely physical activity and sleep duration, has not been adequately explored in adolescents, although a few studies indicated that adequate physical activity may attenuate the effects of increased screen time [32, 37, 38].

Screen-based activities include watching television, playing video games, using a computer, or using a handheld device like a mobile phone, a tablet, or a handheld video game. This brings up the question of whether time spent in front of computers compared with time spent watching television or playing video games has a similar effect on weight. Few studies have examined differences by type of screen-related activity with inconsistent findings, with some indicating that time spent on TV but not on computer is associated with obesity [31–33], while others showing an association between computer time and BMI [34]. The difference in results may be due to the difference in population characteristics, as the first three studies that found no association between computer use and BMI conducted in Germany, Brazil, and Finland, while the study that found an association was conducted in the US.

This study examines the association between two separate types of screen time (using a computer or a handheld device; and watching TV or playing video games) and body mass index (BMI) among US adolescence, as well as difference in these associations by sex. Additionally, we examine the effect medication of meeting daily recommendations for sleep duration and physical activity on the association between screen time and BMI.

Methods

Data for this analysis were derived from the 2016–2017 National Survey of Children’s Health (NSCH), which is a nationally representative survey of U.S. parents and caregivers regarding the health and wellbeing of their non-institutionalized children aged 0–17 years. The survey is sponsored by the Health Resources and Services Administration’s Maternal and Child Health Bureau and conducted annually by the U.S. Census Bureau using mailed invitations sent to randomly selected households. The survey can be completed either by mail or online. A full description of the survey methods can be found elsewhere [24]. This analysis includes adolescents aged 10–17 years whose parents responded to the 2016 or 2017 surveys and who have complete data on BMI and screen time (N = 35,580). Adolescents with moderate to severe autism; brain injury; heart disease; down syndrome; genetic disorders; cerebral palsy; blindness; or significant functional limitation were excluded from the analysis, leading to a final sample size of 29,480. This study received an exemption determination by the Institutional Review Board at the University of South Florida.

Measures

Outcome variable

Obesity was measured by BMI which was calculated based on the child’s height and weight reported by parents. BMI was categorized using age and sex-specific percentiles according to Centers for Disease Control and Prevention (CDC) growth charts as: underweight (less than 5th percentile); healthy weight (5th to 84th percentile); overweight or obese (85^th percentile or above). Healthy weight was used as the reference value.

Exposure variable

Screen time was defined by two different measures constructed using the following questions: 1) On an average weekday, about how much time does this child usually spend with computers, cell phones, handheld video games, and other electronic devices, doing things other than schoolwork? and, 2) On an average weekday, about how much time does this child usually spend in front of a TV watching TV programs, videos, or playing video games? Response options included none, less than one hour, 1 hour, 2–3 hours, 4 hours or more. For the purpose of this analysis, both screen time variables were categorized as: <1 hour per day (reference category), 1–3 hours per day, and ≥4 hours per day.

Effect modifiers

Sleep duration was measured by the parent’s response to the question “During the past week, how many hours of sleep did this child get on an average weeknight?” The sleep duration variable was constructed according to the American Academy of Sleep Medicine’s consensus statement on recommended sleep required for optimal health. According to the statement the recommended sleep duration for children aged 6–12 and 13–18 years is 9–12 hours and 8–10 hours respectively [25]. Based on this recommendation, the sleep duration was divided into two categories: child sleeps recommended age-appropriate hours and child sleeps less than recommended age-appropriate hours.

Physical activity was assessed based on the parent’s response to the question “during the past week, on how many days did this child exercise, play a sport, or participate in physical activity for at least 60 minutes?” The U.S. Department of Health and Human Services’ physical activity guidelines for adolescents [26] recommend at least 60 minutes of physical activity per day, and the variable was categorized as: does not meet the recommended physical activity guidelines or meets the recommended physical activity guidelines.

Covariates were measured based on responses by the parent or guardian of the child, and included child’s age (years), sex (male or female), race/ethnicity (Hispanic, White non-Hispanic, Black non-Hispanic, multi-racial/other non-Hispanic), child health status (excellent, good, fair or poor); depression status (does not have depression, ever told but not current, current depression); anxiety status (does not have anxiety, ever told but not current, current anxiety); and ADHD severity (does not currently have ADHD, mild ADHD, moderate or severe ADHD), in addition to family’s income compared with the federal poverty level FPL (0–99% FPL, 100%-199% FPL, 200%-399% FPL, 400% FPL or above).

Statistical analysis

Frequency and percentage were used to describe the distribution of categorical variables and median and intra-quartile range was used to describe the distribution of age for the entire sample and by BMI. The distributions of categorical and continuous variables were calculated using SAS 9.4 (Cary, NC) procedures, Survey Freq and Survey means, respectively. These procedures incorporate complex survey designs, including designs with stratification, clustering, and unequal weighting Multinomial logistic regression analysis was used to examine the association of each of the screen time variables (TV or video games, computer or handheld device) with BMI categories. The reference group for BMI was normal weight and the reference group for screen time variables was screentime of <1 hour per day. For both screen time variables, we started with an unadjusted model, followed by a multivariable model adjusted for age, sex, race, health status, depression status, anxiety status, ADHD severity and family income. The models were then stratified by sex to explore potential effect heterogeneity by sex. To examine potential effect modification by sleep duration and physical activity, we added two interaction terms separately (screen time*sleep) and (screen time*physical activity) to each of the fully adjusted models, to examine the combined effect of screen time and each of the two variables, compared with the reference value (screen time <1 hour, meets the guidelines of sleep duration/physical activity).These analyses were repeated for the fully adjusted model stratified by sex. Odds ratios (OR) and 95% confidence intervals (CI) were calculated for all associations. The multinomial logistic regression analysis was performed using SAS 9.4 (Carey, SC) Survey logistic procedure.

Results

The study included 29,480 adolescents (49.4% female), with a median age of 13 years. About 30.7% of the participants were overweight/obese, while 6.3% were underweight. Adolescents males had higher prevalence of overweight/obese (32.3% vs 29.0% male) and underweight (7.3% vs 5.0%52.2% male) as compared to adolescent females. Prevalence of overweight/obesity was higher in Hispanic (39.3%) and Black (39.4%) adolescents as compared to white adolescents (29.0). Prevalence of overweight/obesity was higher in adolescents living in poverty (FPL: 0–99%, 38.4%), with fair or poor general health (61.7%), depression (38.4), anxiety (34.9%). The prevalence of overweight/obesity was higher in adolescents who did not get recommended amount of sleep (31.8% vs 25.7%) and physical activity (31.8% vs 25.7%). (Table 1).

Table 1. Characteristics of adolescent participants in the National Survey of Children’s Health.

	Underweight	Healthy weight	Overweight or obese
	N (%)	N (%)	N (%)
	1825 (6.3)	19873 (63.1)	7782 (30.7)
Age, Median (IQR)*	12 (10–14)	13 (11–15)	13 (11–15)
Sex • Male • Female	1013 (7.3) 812 (5.3)	9227 (60.4) 10646 (65.752.6)	4232 (32.3) 3550 (29.0)
Race • Hispanic • White, non-Hispanic • Black, non-Hispanic • Multi-racial/Other, non-Hispanic	202 (5.7) 1258 (6.5) 94 (4.8) 271 (8.9)	1876 (55.1) 14455 (68.6) 1008 (55.8) 2534 (64.5)	1092 (39.3) 5096 (25.0) 668 (39.4) 926 (26.6)
Poverty Level • 0–99% FPL • 100%-199% FPL • 200%-399% FPL • 400% FPL or above	173 (6.9) 260 (5.6) 502 (5.5) 890 (7.0)	1503 (54.9) 2525 (58.4) 5934 (63.2) 9911 (7.07)	946 (38.2) 1405 (36.0) 2562 (31.3) 2869 (22.3)
Child Health Status • Excellent or very good • Good • Fair or poor	1712 (6.4) 96 (5.0) 11 (5.0)	18961 (65.4) 791 (42.3) 75 (33.3)	6785 (28.2) 884 (52.7) 83 (61.7)
Anxiety • Never • Ever, but not current • Current	1629 (6.2) 32 (16.8) 150 (4.7)	17827 (63.4) 319 (54.9) 1610 (60.4)	6790 (30.4) 132 (28.3) 800 (34.9)
Depression • Never • Ever, but not current • Current	1750 (6.4) 16 (3.1) 55 (4.4)	18805 (63.4) 257 (48.7) 727 (57.2)	7114 (30.2) 121 (48.2) 501 (38.4)
ADHD • No current ADHD • Current mild • Current moderate or severe	1592 (6.3) 118 (7.8) 87 (4.9)	17892 (63.0) 977 (62.7) 790 (62.5.)	6912 (30.7) 402 (29.5) 382 (32.6)
Physical Activity • Meets recommendations • Does not meet recommendations	339 (5.5) 1452 (6.4)	3754 (68.8) 15776 (61.8)	1097 (25.7) 6546 (31.8)
Sleep • Meets recommendations • Does not meet recommendations	1279 (6.8) 508 (5.2)	13429 (64.9) 6102 (60.1)	4746 (28.3) 2904 (34.7)
Time Spent with Computers/ Handheld Device • None or less than 1 hour per day • 1–3 hours per day • 4 hours or more per day	290 (7.6) 1223 (6.4) 296 (5.0)	2470 (64.9) 13755 (64.1) 3487 (58.4)	802 (27.5) 5141 (29.5) 1773 (36.6)
Time Spent Watching TV /Playing Video Games • None or less than 1 hour per day • 1–3 hours per day • 4 hours or more per day	394 (6.3) 1255 (6.5) 162 (5.2)	4505 (71.6) 13622 (62.4) 1582 (51.0)	1226 (22.1) 5440 (31.1) 1043 (43.8)

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Adolescents spending ≥4 hours with a computer or handheld device had the highest prevalence of overweight/obesity (36.6%) compared with 29.5% in adolescents spending 1–3 hours and 27.5% in adolescents spending <1 hour with a computer or handheld device. A similar pattern was observed in time spent watching TV or playing video games. (Table 1).

Table 2 shows the adjusted ORs of the association between time spent watching TV or playing video games and having overweight/obesity. It shows that spending 1–3 hours (vs. <1 hour) watching TV or playing video games was associated with a 40% increase in the odds of having overweight/obesity (Adjusted OR = 1.4, 95% CI 1.19, 1.65). Similarly, spending ≥4 hours (vs <1 hour) watching TV or playing video games was associated with 2.19 times likelihood of having overweight/obesity (adjusted OR = 2.19, 95% CI 1.73, 2.77). The association between time spent watching TV or playing video games and having overweight/obesity stratified by sex also showed similar results (Table 2). Table 3 shows the association between time spent using a computer or handheld device and having overweight/obesity for all participants and separately for males and females. Using a computer or handheld device was associated with having overweight/obesity in adolescents who spent ≥4 hours (vs <1 hour) on these devices (OR = 1.53, 95% CI 1.19, 1.97). Spending ≥4 hours (vs <1 hour) using a computer or handheld device was associated with having overweight/obesity in adolescent females (OR = 1.71, 95% CI 1.2, 2.44), but not in adolescent males (OR = 1.4, 95% CI 0.99, 1.99).

Table 2. Results of logistic regression analysis of the association between time spent watching TV or playing video games and BMI in adolescents.

	Overweight or obese vs. Normal BMI
	Total		Males		Females
Hours spent watching TV or playing video games per day	Unadjusted OR (95% CI)	Adjusted OR (95% CI)^*	Unadjusted OR (95% CI)	Adjusted OR (95% CI)^*	Unadjusted OR (95% CI)	Adjusted OR (95% CI)^*
None or less than 1 hour per day	1.00	1.00	1.00	1.00	1.00	1.00
1–3 hours per day	1.61 (1.37, 1.9)	1.4 (1.19, 1.65)	1.42 (1.09, 1.86)	1.33 (1.03, 1.71)	1.70 (1.39, 2.09)	1.45 (1.18, 1.78)
4 hours or more per day	2.78 (2.19, 3.52)	2.19 (1.73, 2.77)	2.20 (1.57, 3.10)	1.92 (1.39, 2.63)	3.72 (2.56, 5.41)	2.56 (1.82, 3.61)

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*Adjusted for age, sex, race, health status, family income, depression, anxiety, ADHD, and amount of sleep and physical activity.

Table 3. Results of logistic regression analysis of the association between time spent with computers, cell phones, handheld video games and other electronic devices, and BMI in adolescents.

	Overweight or obese vs. Normal BMI
	Total		Males		Females
Hours spent with a computer or handheld device per day	Unadjusted OR (95% CI)	Adjusted OR (95% CI)*	Unadjusted OR (95% CI)	Adjusted OR (95% CI)*	Unadjusted OR (95% CI)	Adjusted OR (95% CI)*
None or less than 1 hour per day	1.0	1.0	1.0	1.0	1.0	1.0
1–3 hours per day	1.09 (0.88, 1.34)	1.17 (0.94, 1.47)	1.11 (0.82, 1.49)	1.18 (0.86, 1.61)	1.06 (0.79, 1.44)	1.2 (0.88, 1.64)
4 hours or more per day	1.48 (1.17, 1.87)	1.53 (1.19, 1.97)	1.32 (0.95, 1.85)	1.4 (0.99, 1.99)	1.69 (1.21, 2.37)	1.71 (1.2, 2.44)

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^a Adjusted for age, sex, race, health status, family income, depression, anxiety, ADHD, and amount of sleep and physical activity.

Table 4 shows the association between time spent watching TV or playing video games and having overweight/obesity by physical activity, with adolescents spending <1 hour watching TV or playing video games and meeting the guidelines for recommended physical activity as the reference. Adolescents spending 1–3 hours watching TV or playing video games and not meeting the guidelines for recommended physical activity were 1.88 (95% CI: 1.34, 2.62) times likely to have overweight/obesity. Likewise, adolescents spending ≥4 hours watching TV or playing video games and not meeting the guidelines for recommended physical activity were 3.04 (95% CI:2.10, 4.40) times more likely to have overweight/obesity. Adolescent females not meeting the guidelines for required physical activity and spending <1 hour, 1–3 hours or ≥4 hours watching TV or playing video games were 1.71 (95% CI: 1.03, 2.84), 2.40 (95% CI: 1.48, 3.9) and 4.28 (95% CI: 2.45, 7.49) times likely to have overweight/obesity, respectively (Table 4). Similarly, adolescent males not meeting the guidelines for required physical activity and spending 1–3 hours or ≥4 hours watching TV or playing video games were 1.57 (95% CI: 1.01, 2.45), and 2.35 (95% CI:1.45, 3.81) times likely to have overweight/obesity, respectively (Table 4).

Table 4. Results of logistic regression analysis of the effect modification by physical activity on the association between time spent watching TV or playing video games and overweight.

Overweight or obese vs. Normal BMI
		Total	Males	Females
Physical Activity	Hours spent watching TV or playing video games per day	Adjusted OR (95% CI)*	Adjusted OR (95% CI)*	Adjusted OR (95% CI)*
Meets guidelines	None or less than 1 hour per day	1.00	1.00	1.00
Meets guidelines	1–3 hours per day	1.36 (0.93, 1.99)	1.14 (0.69, 1.88)	1.74 (0.99, 3.03)
Meets guidelines	4 hours or more per day	1.64 (0.72, 3.72)	1.34 (0.54, 3.33)	3.02 (1.04, 8.75)
Does not meet guidelines	None or less than 1 hour per day	1.33 (0.93, 1.91)	1.12 (0.67, 1.85)	1.71 (1.03, 2.84)
Does not meet guidelines	1–3 hours per day	1.88 (1.34, 2.62)	1.57 (1.01, 2.45)	2.40 (1.48, 3.9)
Does not meet guidelines	4 hours or more per day	3.04 (2.10, 4.40)	2.35 (1.45, 3.81)	4.28 (2.45, 7.49)

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^a Adjusted for age, sex, race, health status, family income, depression, anxiety, ADHD, and amount of sleep and physical activity.

Table 5 shows the association between hours spent with a computer or handheld device and having overweight/obesity. Adolescents spending ≥4 hours with a computer or handheld device and not meeting the guidelines for recommended physical activity were 1.71 (95% CI: 1.08, 2.70) times likely to have overweight/obesity compared with adolescents spending <1 hour with a computer or handheld device and meeting the guidelines for recommended physical activity. Adolescent females spending ≥4 hours with a computer or handheld device and not meeting the guidelines for recommended physical activity were twice likely to have overweight/obesity (OR = 2.15, 95% CI 1.08, 4.28) as compared to adolescent females spending <1 hour with a computer or handheld device and meeting the guidelines for recommended physical activity.

Table 5. Results of logistic regression analysis of the effect modification by physical activity on the association between time spent using a computer or handheld device and overweight.

Overweight or obese vs. normal BMI
		Total	Males	Females
Physical Activity	Hours spent with a computer or handheld device per day	Adjusted OR (95% CI)*	Adjusted OR (95% CI)*	Adjusted OR (95% CI)*
Meets guidelines	None or less than 1 hour per day	1.00	1.00	1.00
Meets guidelines	1–3 hours per day	0.86 (0.54, 1.38)	0.79 (0.43, 1.43)	1.00 (0.49, 2.08)
Meets guidelines	4 hours or more per day	0.76 (0.41, 1.43)	0.78 (0.36, 1.71)	0.72 (0.29, 1.77)
Does not meet guidelines	None or less than 1 hour per day	0.94 (0.59, 1.53)	0.82 (0.44, 1.53)	1.12 (0.54, 2.31)
Does not meet guidelines	1–3 hours per day	1.24 (0.80, 1.92)	1.16 (0.65, 2.04)	1.41 (0.72, 2.75)
Does not meet guidelines	4 hours or more per day	1.71 (1.08, 2.70)	1.42 (0.79, 2.58)	2.15 (1.07, 4.28)

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^a Adjusted for age, sex, race, health status, family income, depression, anxiety, ADHD, and amount of sleep and physical activity.

Time spent watching TV or playing video games was significantly associated with overweight regardless of whether the child sleeps for the recommended time. Similar patterns were observed when examining the interaction between sleep and the use of computer/handheld device variable (S1 Table).

Discussion

Using recent data from the National Study of Children’s Health, we sought to quantify the relationship between time spent on different types of screens and overweight/obesity in adolescence, and whether this relationship differs by sex, physical activity, or sleep duration. Overall, our findings support the hypothesis that time spent on screens is associated with overweight/obesity, however, the association is more pronounced when the time is spent watching television or playing video games than it is when using computers or handheld devices. Physical activity, and to a lesser extent, sex, appear to modify this association, while sleep duration does not. Our findings add to previous studies that examined the association between screen time and obesity in adolescence and provides additional evidence regarding different types of screen activity and the potential role of adequate physical activity in ameliorating some of the negative effects of increased screen time.

Although we are unable to claim a causal relationship between screen time and overweight/obesity, our findings, combined with prior research, highlight the importance of adequate physical activity for adolescents who spend considerable amounts of time engaged with various types of screens. The highest odds of overweight or obese BMI were in adolescents, particularly females, who spent four or more hours on screens and who do not meet the recommended guidelines for physical activity. Adolescents who met the recommended time for physical activity had no increase in the odds of having overweight or obesity even with four or more hours of screen time, except females with four or more hours of TV/video games time, in whom the odds were attenuated with physical activity, but remain elevated. Encouraging daily physical activity, accompanied by a healthy diet, can be important and effective strategy to combat the adverse effects of prolonged hours spent sitting in front of a computer or using a tablet. Moreover, with the shift in screen time habits in adolescents in recent years, and more time being spent using computers and handheld devices compared with television viewing [28], our results add to the much-needed knowledge regarding the impact of these new types of screen activities on adolescent BMI. Furthermore, with the difficulty in limiting the time that adolescents spend in front of screens, whether for education, socializing, or leisure, it is now more important than ever to identify factors that reduce the negative effects of screen time, in particular by encouraging adequate physical activity.

Our findings are consistent with prior research showing an association between screen time or TV viewing and obesity among adolescents [10, 27, 28], and with some studies that examined computer time separately [29–31], most of which found little to no association between time spent using a computer and overweight in adolescence. Most of these studies were conducted in children under the age of 12, with only few including adolescents. By contrast, a study by Utter et al. (2003) [34] of high school students found that high computer use (four or more hours per day) was associated with higher mean BMI in boys. However, this study did not adjust for physical activity or health status, and was conducted during the 1998–1999 school year, when access to computers and the internet was still limited and participants spent an average of 1–1.25 hours per day on computers. Our present study using recent data is more representative of current screen time habits in adolescents.

Differences related to screen activity type may be explained by other activities done at the same time, such as eating while watching television, which is less likely when using the computer [32]. Additionally, TV viewing is a passive activity that often takes an hour or more at a time, while most computer use requires active interaction, and may be done for short but recurring periods. Moreover, TV viewing exposes adolescents to unhealthy food advertising, which may affect their dietary habits [33]. Along with sedentary behavior, these factors may contribute to overweight. It is important to note however, that TV viewing in the present study was combined with video game playing in one variable, which prevents us from making conclusions regarding which activity may have had the stronger association with BMI. Likewise, computer use was combined with use of handheld devices, which unlike computers, may be used while sitting, standing, or walking.

Sex-related differences in the association between screen time and BMI observed in our study were somewhat similar to the results observed by Kautiainen et al. [31], who found a significant association between screen time and BMI in 14–18 year-old girls but not in boys. Slightly different pattern was observed in Utter et al’s [34] study of middle and high school students, which found TV viewing to be significant in males and females, and computer time to be significant only in females. Likewise, Te Velde et al. [29] found a significant association between screen time and BMI in 9–14 year-old males and females. The three studies mentioned above were conducted more than 10 years prior to our data collection, which may explain some of the observed differences due to the change in types of screen activities in adolescents over time. Furthermore, differences in results may be due to different age group, measurement of screen time, and statistical methods used in each study. By contrast, a recent study by Sampasa-Kanyinga et al. (2020) found the association between social media use and BMI to be significant in males in 7^th-12^th grade, but not in females of the same age [35]. Nevertheless, the pattern observed in our study is consistent with studies in adults, which found a stronger association between time spent watching TV and metabolic syndrome in women than in men [36, 37]. More research is needed to understand the impact of different types of screen time on BMI in males and females.

The observed effect modification by physical activity is similar to what Mutunga et al. [38] observed, but in their study, the attenuating effect of increased physical activity was only observed in adolescents with high socioeconomic status. Likewise, a recent study of Canadian children aged 7–12 found that sedentary time was significantly associated with BMI only in children who do not meet the physical activity guidelines [39]. Additionally, the systematic review by Rey-Lopez and colleagues [30] concluded that unlike TV viewing, “playing video games and using computers do not represent such a high risk if these do not replace too much physical activity”, indicating potential effect modification by physical activity. Although we are not able to determine whether screen time is replacing physical activity in our cross-sectional study, this may be a plausible explanation of the effect modification we observed in our study, considering that adolescents who find the time for daily physical activity despite spending ≥4 hours on screens have significantly lower odds of obesity compared with those who are less frequent active. Furthermore, the energy expenditure associated with daily physical activity compensates, at least partially, for the sedentary periods spent on screens.

Our study has many strengths, including the large representative sample of US adolescents, use of recent data examining new types of technology and current media use behavior, and the examination of different types of screen activities in males and females and effect modification by physical activity. Nonetheless, there are some limitations that must be noted. The cross-sectional design does not permit the determination of causality between time spent on screens, physical activity, and BMI; therefore, our findings need to be confirmed with longitudinal studies. Additionally, time spent on screens was reported by parents of the adolescents, which may lead to misclassification of the exposure. A study of Canadian children aged 9–11 found that parental and child reports or TV viewing time were generally concordant, however parents consistently reported less TV exposure than children [40]. Although this study was in a younger age group, it is logical to assume that parental report will generally underestimate screen time in adolescent, given the increasing independence of adolescents as they age. As long as this underestimation is independent of weight status, it will lead to an underestimation of the measure of association. Likewise, height and weight were reported by parents, which may lead to underestimation of BMI, especially in females [41], although a study by Goodman et al. [42] found parental report of height and weight is better than teen report, and correctly classifies 96% as to obesity status. One important limitation is the combination of TV watching and video game use in one variable. Television viewing is mostly a passive activity that does not lead to energy expenditure and is frequently accompanied by eating or snaking. By contrast, playing video games requires physical interaction with the controls, which may not allow eating or snacking, and some games include some physical activity (active video games). Therefore, we are unable to determine the impact of television viewing and video game playing separately. Likewise, computers and handheld devices may differ in the context of their use and their impact on weight. Furthermore, the question regarding time spent on computers specified time doing activities other than schoolwork, which limits the generalizability of our findings. Finally, this study uses data collected in 2016–2017, which may not be reflective of current screen-based practices, especially considering the Covid-19 pandemic and the shift to online education.

Conclusion

The results of our study indicate that time spent in front of screens, is significantly associated with obesity in US children and adolescents. Our findings add to the literature by showing that this association is stronger in females compared with males, and when watching television or playing video games compared with computers or handheld devices, but weaker in those with 60 minutes or more of physical activity every day. Given the increasing utilization of screen-based activities, it is important to encourage adequate physical activity in adolescents, particularly females, which may ameliorate some of the effects of extended screen time. Future research is needed to confirm the findings and to test interventions that encourage reduced screen time and increased physical activity among adolescents.

Supporting information

S1 Table. Results of logistic regression analysis of the effect modification by sleep duration on the association between time spent watching TV or playing video games and overweight.

(DOCX)

Click here for additional data file.^{(30.5KB, docx)}

Abbreviations

TV: television
OR: odds ratio
CI: confidence interval
BMI: body mass index
U.S.: United States
NSCH: National Survey of Children’s Health (NSCH)

Data Availability

Data used in this study came from the National Survey of Children's Health. Data can be requested from the Data Resource Center for Child and Adolescent Health: https://www.childhealthdata.org/help/dataset.

Funding Statement

The author(s) received no specific funding for this work.

References

1.World Health Organization. Commission on Ending Childhood Obesity [Internet]. WHO. World Health Organization; [cited 2020. Nov 15]. Available from: http://www.who.int/end-childhood-obesity/en/ [Google Scholar]
2.Centers for Disease Control and Prevention. Childhood Obesity Facts [Internet]. Overweight and Obesity. 2022. [cited 2022 Aug 31]. Available from: https://www.cdc.gov/obesity/data/childhood.html [Google Scholar]
3.Bryan S, Afful J, Carroll M, Te-Ching C, Orlando D, Fink S, et al. National Health and Nutrition Examination Survey 2017–March 2020 Pre-pandemic Data Files [Internet]. National Center for Health Statistics (U.S.); 2021 Jun [cited 2022 Aug 31]. Available from: https://stacks.cdc.gov/view/cdc/106273 [Google Scholar]
4.Abarca-Gómez L, Abdeen ZA, Hamid ZA, Abu-Rmeileh NM, Acosta-Cazares B, Acuin C, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. The Lancet [Internet]. 2017 Dec [cited 2020 Nov 15];390(10113):2627–42. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0140673617321293 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Centers for Disease Control and Prevention. Causes and Consequences of Childhood Obesity [Internet]. Centers for Disease Control and Prevention. 2020. [cited 2020 Nov 16]. Available from: https://www.cdc.gov/obesity/childhood/causes.html [Google Scholar]
6.Cureau FV, Sparrenberger K, Bloch KV, Ekelund U, Schaan BD. Associations of multiple unhealthy lifestyle behaviors with overweight/obesity and abdominal obesity among Brazilian adolescents: A country-wide survey. Nutrition, Metabolism and Cardiovascular Diseases [Internet]. 2018. Jul [cited 2020 Nov 16];28(7):765–74. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0939475318301510 [DOI] [PubMed] [Google Scholar]
7.Hu EY, Ramachandran S, Bhattacharya K, Nunna S. Obesity Among High School Students in the United States: Risk Factors and Their Population Attributable Fraction. Prev Chronic Dis [Internet]. 2018. Nov 8 [cited 2020 Nov 16];15:180122. Available from: http://www.cdc.gov/pcd/issues/2018/18_0122.htm doi: 10.5888/pcd15.180122 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Menon S, Philipneri A, Ratnasingham S, Manson H. The integrated role of multiple healthy weight behaviours on overweight and obesity among adolescents: a cross-sectional study. BMC Public Health [Internet]. 2019. Dec [cited 2020 Nov 16];19(1):1157. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-019-7007-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Mihrshahi S, Drayton BA, Bauman AE, Hardy LL. Associations between childhood overweight, obesity, abdominal obesity and obesogenic behaviors and practices in Australian homes. BMC Public Health [Internet]. 2018. Dec [cited 2020 Nov 16];18(1):44. Available from: http://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-017-4595-y [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Fang K, Mu M, Liu K, He Y. Screen time and childhood overweight/obesity: A systematic review and meta‐analysis. Child Care Health Dev [Internet]. 2019. Sep [cited 2020 Nov 17];45(5):744–53. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/cch.12701 [DOI] [PubMed] [Google Scholar]
11.Friel CP, Duran AT, Shechter A, Diaz KM. U.S. Children Meeting Physical Activity, Screen Time, and Sleep Guidelines. Am J Prev Med. 2020. Oct;59(4):513–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Anderson M, Jiang J. Teens, Social Media and Technology 2018 [Internet]. Pew Research Center: Internet, Science & Tech. 2018. [cited 2022 Apr 13]. Available from: https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/ [Google Scholar]
13.Marshall SJ, Biddle SJH, Gorely T, Cameron N, Murdey I. Relationships between media use, body fatness and physical activity in children and youth: A meta-analysis. International Journal of Obesity. 2004. Oct;28(10):1238–46. doi: 10.1038/sj.ijo.0802706 [DOI] [PubMed] [Google Scholar]
14.Castro JAC, Nunes HEG, Silva DAS. Prevalence of abdominal obesity in adolescents: association between sociodemographic factors and lifestyle. Revista Paulista de Pediatria (English Edition) [Internet]. 2016. Sep [cited 2020 Nov 18];34(3):343–51. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2359348216000166 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Christofaro DGD, Andrade SMD, Mesas E, Fernandes RA, Júnior JCF. Higher screen time is associated with overweight, poor dietary habits and physical inactivity in Brazilian adolescents, mainly among girls.: 10. doi: 10.1080/17461391.2015.1068868 [DOI] [PubMed] [Google Scholar]
16.Lajous M, Chavarro J, Peterson KE, Hernández-Prado B, Cruz-Valdéz A, Hernández-Ávila M, et al. Screen time and adiposity in adolescents in Mexico. Public Health Nutr [Internet]. 2009. Oct [cited 2020 Nov 18];12(10):1938–45. Available from: https://www.cambridge.org/core/product/identifier/S1368980009004881/type/journal_article doi: 10.1017/S1368980009004881 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Lee S, Wong J, Shanita S, Ismail M, Deurenberg P, Poh B. Daily Physical Activity and Screen Time, but Not Other Sedentary Activities, Are Associated with Measures of Obesity during Childhood. IJERPH [Internet]. 2014. Dec 23 [cited 2020 Nov 18];12(1):146–61. Available from: http://www.mdpi.com/1660-4601/12/1/146 doi: 10.3390/ijerph120100146 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Maher C, Olds TS, Eisenmann JC, Dollman J. Screen time is more strongly associated than physical activity with overweight and obesity in 9- to 16-year-old Australians: Overweight in adolescents—screen time or physical activity? Acta Paediatrica [Internet]. 2012. Nov [cited 2020 Nov 18];101(11):1170–4. Available from: http://doi.wiley.com/10.1111/j.1651-2227.2012.02804.x [DOI] [PubMed] [Google Scholar]
19.Phan TLT, Tucker JM, Siegel R, Christison AL, Stratbucker W, Werk LN, et al. Electronic Gaming Characteristics Associated with Class 3 Severe Obesity in Youth Who Attend the Pediatric Weight Management Programs of the COMPASS Network. Childhood Obesity [Internet]. 2019. Jan [cited 2020 Nov 16];15(1):21–30. Available from: https://www.liebertpub.com/doi/10.1089/chi.2018.0156 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Phuong Van Ngoc Nguyen, Tang Kim Hong, Dung The Nguyen, Annie R Robert. Excessive screen viewing time by adolescents and body fatness in a developing country: Vietnam. Asia Pacific Journal of Clinical Nutrition. 2016. Jan 1;25(1). [DOI] [PubMed] [Google Scholar]
21.Boone JE, Gordon-Larsen P, Adair LS, Popkin BM. Screen time and physical activity during adolescence: longitudinal effects on obesity in young adulthood. Int J Behav Nutr Phys Act [Internet]. 2007. [cited 2020 Nov 16];4(1):26. Available from: http://ijbnpa.biomedcentral.com/articles/10.1186/1479-5868-4-26 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Mitchell JA, Rodriguez D, Schmitz KH, Audrain-McGovern J. Greater screen time is associated with adolescent obesity: A longitudinal study of the BMI distribution from Ages 14 to 18: Screen Time and BMI from Ages 14 to 18. Obesity [Internet]. 2013. Mar [cited 2020 Nov 18];21(3):572–5. Available from: http://doi.wiley.com/10.1002/oby.20157 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Hands BP, Chivers PT, Parker HE, Beilin L, Kendall G, Larkin D. The associations between physical activity, screen time and weight from 6 to 14 yrs: The Raine Study. Journal of Science and Medicine in Sport [Internet]. 2011. Sep [cited 2020 Nov 16];14(5):397–403. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1440244011000739 doi: 10.1016/j.jsams.2011.03.011 [DOI] [PubMed] [Google Scholar]
24.NSCH Survey Methodology—Data Resource Center for Child and Adolescent Health [Internet]. [cited 2021. Mar 24]. Available from: https://www.childhealthdata.org/learn-about-the-nsch/methods [Google Scholar]
25.Paruthi S, Brooks LJ, D ‘Ambrosio Carolyn, Hall WA, Kotagal S, Lloyd RM, et al. Recommended Amount of Sleep for Pediatric Populations: A Consensus Statement of the American Academy of Sleep Medicine. Journal of Clinical Sleep Medicine. 12(06):785–6. doi: 10.5664/jcsm.5866 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Physical Activity Guidelines for Americans, 2nd edition.: 118. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.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. Nov;140(Suppl 2):S97–101. doi: 10.1542/peds.2016-1758K [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Zhu Z, Tang Y, Zhuang J, Liu Y, Wu X, Cai Y, et al. Physical activity, screen viewing time, and overweight/obesity among Chinese children and adolescents: An update from the 2017 physical activity and fitness in China—The youth study. BMC Public Health. 2019. Feb;19(1). doi: 10.1186/s12889-019-6515-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Te Velde SJ, De Bourdeaudhuij I, Thorsdottir I, Rasmussen M, Hagströmer M, Klepp KI, et al. Patterns in sedentary and exercise behaviors and associations with overweight in 9-14-year-old boys and girls—A cross-sectional study. BMC Public Health. 2007. Dec;7(1):16–16. doi: 10.1186/1471-2458-7-16 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Rey-López JP, Vicente-Rodríguez G, Biosca M, Moreno LA. Sedentary behaviour and obesity development in children and adolescents. Nutrition, Metabolism and Cardiovascular Diseases. 2008. Mar;18(3):242–51. doi: 10.1016/j.numecd.2007.07.008 [DOI] [PubMed] [Google Scholar]
31.Kautiainen S, Koivusilta L, Lintonen T, Virtanen SM, Rimpelä A. Use of information and communication technology and prevalence of overweight and obesity among adolescents. International Journal of Obesity. 2005. Aug;29(8):925–33. doi: 10.1038/sj.ijo.0802994 [DOI] [PubMed] [Google Scholar]
32.Raptou E, Papastefanou G, Mattas K. Investigating the influence of eating habits, body weight and television programme preferences on television viewing time and domestic computer usage. Perspect Public Health. 2017. Jan 1;137(1):59–72. doi: 10.1177/1757913916636925 [DOI] [PubMed] [Google Scholar]
33.Ld D, Wr T, Das S, Fcs G, J M, Dgd C. Food advertisements on television and eating habits in adolescents: a school-based study. Revista de saude publica [Internet]. 2020. [cited 2022 Apr 13];54. Available from: https://pubmed.ncbi.nlm.nih.gov/32491114/ [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Utter J, Neumark-Sztainer D, Jeffery R, Story M. Couch potatoes or French fries: Are sedentary behaviors associated with body mass index, physical activity, and dietary behaviors among adolescents? Journal of the American Dietetic Association. 2003. Oct;103(10):1298–305. doi: 10.1016/s0002-8223(03)01079-4 [DOI] [PubMed] [Google Scholar]
35.Sampasa-Kanyinga H, Colman I, Goldfield GS, Hamilton HA, Chaput JP. Sex differences in the relationship between social media use, short sleep duration, and body mass index among adolescents. Sleep Health. 2020. Oct;6(5):601–8. doi: 10.1016/j.sleh.2020.01.017 [DOI] [PubMed] [Google Scholar]
36.Dunstan DW, Salmon J, Owen N, Armstrong T, Zimmet PZ, Welborn TA, et al. Associations of TV viewing and physical activity with the metabolic syndrome in Australian adults. Diabetologia. 2005. Nov 1;48(11):2254–61. doi: 10.1007/s00125-005-1963-4 [DOI] [PubMed] [Google Scholar]
37.Bertrais S, Beyeme-Ondoua JP, Czernichow S, Galan P, Hercberg S, Oppert JM. Sedentary Behaviors, Physical Activity, and Metabolic Syndrome in Middle-aged French Subjects. Obesity Research. 2005;13(5):936–44. doi: 10.1038/oby.2005.108 [DOI] [PubMed] [Google Scholar]
38.Mutunga M, Gallagher AM, Boreham C, Watkins DC, Murray LJ, Cran G, et al. Socioeconomic differences in risk factors for obesity in adolescents in Northern Ireland. International Journal of Pediatric Obesity. 2006. Jan 1;1(2):114–9. doi: 10.1080/17477160600569560 [DOI] [PubMed] [Google Scholar]
39.Crowe M, Sampasa-Kanyinga H, Saunders TJ, Hamilton HA, Benchimol EI, Chaput JP. Combinations of physical activity and screen time recommendations and their association with overweight/obesity in adolescents. Can J Public Health. 2020. Aug 1;111(4):515–22. doi: 10.17269/s41997-020-00313-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Tudor-Locke C, Craig CL, Cameron C, Griffiths JM. Canadian children’s and youth’s pedometer-determined steps/day, parent-reported TV watching time, and overweight/obesity: The CANPLAY Surveillance Study. Int J Behav Nutr Phys Act. 2011. Jun 25;8:66. doi: 10.1186/1479-5868-8-66 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Brault MC, Turcotte O, Aimé A, Côté M, Bégin C. Body Mass Index Accuracy in Preadolescents: Can We Trust Self-Report or Should We Seek Parent Report? J Pediatr. 2015. Aug;167(2):366–71. doi: 10.1016/j.jpeds.2015.04.043 [DOI] [PubMed] [Google Scholar]
42.Goodman E, Hinden BR, Khandelwal S. Accuracy of teen and parental reports of obesity and body mass index. Pediatrics. 2000. Jul;106(1 Pt 1):52–8. doi: 10.1542/peds.106.1.52 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0278490.r001

Decision Letter 0

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1 Aug 2022

PONE-D-22-15444Association between screen time and obesity in US adolescents: A cross-sectional analysis using National Survey of Children’s Health 2016-2017PLOS ONE

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Reviewer #1: This manuscript examines associations between screen-use and obesity, and examines ineractions with physical activity and sleep in a large sample of adolescents from the US. While this is a generally well written manuscript, there are a number of comments for your consideration below:

Abstract:

A little untidy with some mistakes/incomplete information. For example, in the introduction you state that you examine effect modification by sleep and PA, then in the methods you state sex and pa. furthermore, you examine the associations between screen time and BMI not the other way around. Consistency in ordering is necessary for the reader to understand what you have done. Nothing in the methods section about how behaviours etc were measured. Results are missing the n and seem quite brief. Consider stating pa not exercise as you measured whether or not adolescents met the pa guidelines, not exercise.

Introduction:

I found this section lacking in a clear and concise rational.

The data you present on obeisty is quite out of date.

The second paragraph is very disjointed with talk of screen time then sedentary behaviour, but no definition or distinction described.

No rational for sleep or pa as effect modifiers.

I don't think the rational around covid is appropriate here given that your data preceed covid.

Methods:

This section is relatively well described.

We aren't told how covariates were measured and given that you describe them in results, the reader should know how they were measured.

Statistical analysis section is very brief. i would expect more on why you chose this method to look at effect modification and more descripton. also the ordering seems strange. Typically you would see if your main variables differ by gender at the start and then all subsequent analyses are conducted by gender.

Results:

This section is too long and the tables could be better presented by having gender as columns not extra rows where variabls are repeated.

First paragraph has a mistake in th % in second sentence.

Discussion:

Lacking in discussion and implicatations.

Limitations need expanding to add in that this data is >6 years old and not reflective of current screen-based practices.

Using the phrase exercise is unclear.

Bottom paragraph on page 19 is unclear when you are talking of replacement of pa by screen-time. Your analysis doesnt allow such statements to be made. you could allude to isotemporal substitution, but not suggest that that is what is hapenning here.

No conclusion?

what do your findings mean, to whom and what shall we do now that we have them?

Reviewer #2: Bakour and colleagues analyzed the 2016-2017 National Survey of Children’s Health and assessed the association between time spent watching TV, playing video games, using a computer or handheld device, and BMI among U.S. adolescents and potential effect modification of these associations by sleep duration and physical activity. The study has merit but I have several questions/comments:

1. Abstract: Please elaborate what does aOR mean? Then you can use the abbreviation.

2. Abstract: Line 39: Please write ‘95% CI’ before using 95% confidence interval.

3. After using an odds ratio, please put a semicolon before mentioning 95% CI. For example in Line 39 please use 1.53; 95% CI: 1.19, 1.97.

4. Before using Ordinal logistic regression, was proportional odds assumption verified by the authors?

5. In Table 1, please use row percentages instead of column percentages. That will give some idea about the prevalence of each weight categories.

6. In table 2 and 3, please report the unadjusted odds for male and female.

7. In Page 14, what is the title of the table?

8. In Table 3, why significant association was found for females but not for males? Please discuss.

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Reviewer #1: No

Reviewer #2: Yes: Rajat Das Gupta, University of South Carolina

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PLoS One. 2022 Dec 1;17(12):e0278490. doi: 10.1371/journal.pone.0278490.r002

Author response to Decision Letter 0

15 Sep 2022

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

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Response: Thank you for your comment. The manuscript meets PLOS ONE’s style requirements. Please let us know if additional changes are necessary.

Response: This study was part of a larger project that included secondary analyses of the National Survey of Children’s Health. The project title was “Influence of social determinants on child health: A secondary analysis of the National Survey of Children’s Health”, PI: Dr. Ronee Wilson, a co-author on this manuscript. The project was submitted to USF IRB in 2018 and received an exempt status (does not meet the definition of human subject research). IRB#: Pro00035299. The IRB letter is uploaded under “other” files, and the statement in the manuscript has been edited for clarification.

In your revised cover letter, please address the following prompts:

We will update your Data Availability statement on your behalf to reflect the information you provide.

Response: The dataset is owned by the Child and Adolescent Health Measurement Initiative (CAHMI), The Johns Hopkins Bloomberg School of Public Health, Department of Population, Family & Reproductive Health. Data may be requested from https://www.childhealthdata.org/dataset.

Reviewers' comments:

Reviewer #1:

This manuscript examines associations between screen-use and obesity, and examines ineractions with physical activity and sleep in a large sample of adolescents from the US. While this is a generally well written manuscript, there are a number of comments for your consideration below:

1. Abstract:

Response: We agree with the reviewers and would like to thank them for pointing out the errors in the abstract. We have updated the abstract to correct the errors and add the missing information.

2. Introduction:

I found this section lacking in a clear and concise rational.

The data you present on obeisty is quite out of date.

The second paragraph is very disjointed with talk of screen time then sedentary behaviour, but no definition or distinction described.

No rational for sleep or pa as effect modifiers.

I don't think the rational around covid is appropriate here given that your data preceded covid.

Response: We have made following changes to the introduction based on your feedback:

• More recent data on obesity statistics is presented.

• Reorganized the introduction to make it clear

• Removed the description about sedentary behavior

• Included rationale for examining sleep and PA as effect modifiers.

• Removed the mention of COVID-19 from the introduction.

3. Methods:

This section is relatively well described.

We aren't told how covariates were measured and given that you describe them in results, the reader should know how they were measured.

Statistical analysis section is very brief. i would expect more on why you chose this method to look at effect modification and more description. also the ordering seems strange. Typically you would see if your main variables differ by gender at the start and then all subsequent analyses are conducted by gender.

Response: Thank you for your feedback. Based on your suggestions, the following changes were made to the methods section:

• A short description of covariate measurement was added.

• Details were added to the statistical analysis section.

• Interaction terms are commonly used in regression analysis to examine the interaction/effect modification between two risk factors. We presented the results in accordance with the recommendation by Knol and VanderWeele (2012). https://academic.oup.com/ije/article/41/2/514/692957. More details were added to the methods section

• Table 1 has been updated to include row percentages, which show the distribution of BMI categories by sex. Given the difference in the prevalence of overweight in males and females, we decided to present regression analysis results in the total sample, and stratified by sex.

4. Results:

This section is too long and the tables could be better presented by having gender as columns not extra rows where variabls are repeated.

First paragraph has a mistake in th % in second sentence.

Response: Thank you for your comment. Phrases and sentences that were repetitive were removed to shorten the section. Table were updated to wide format removing extra rows.

5. Discussion:

Lacking in discussion and implicatations.

Limitations need expanding to add in that this data is >6 years old and not reflective of current screen-based practices.

Using the phrase exercise is unclear.

No conclusion?

what do your findings mean, to whom and what shall we do now that we have them?

Response: We have edited the discussion section to add the following:

• Additional discussion of the implications

• The timing of the survey added as a limitation

• The sentences related to replacement of PA by screen time were rewritten based on your suggestions

• A conclusion section is added at the end.

Reviewer #2:

Bakour and colleagues analyzed the 2016-2017 National Survey of Children’s Health and assessed the association between time spent watching TV, playing video games, using a computer or handheld device, and BMI among U.S. adolescents and potential effect modification of these associations by sleep duration and physical activity. The study has merit but I have several questions/comments:

1. Abstract: Please elaborate what does aOR mean? Then you can use the abbreviation.

Response: Thank you for your comment. We have updated the abstract based on your comment.

2. Abstract: Line 39: Please write ‘95% CI’ before using 95% confidence interval.

We have updated the abstract based on your comment.

3. After using an odds ratio, please put a semicolon before mentioning 95% CI. For example in Line 39 please use 1.53; 95% CI: 1.19, 1.97.

Response: We have made the recommended change to the abstract.

4. Before using Ordinal logistic regression, was proportional odds assumption verified by the authors?

Response: Thank you very much for your comment. The association between screen time and BMI was examined using multinomial logistic regression model with normal weight as the reference category. The methods section incorrectly listed ordinal instead of multinomial. This error has been correct in the revised manuscript.

5. In Table 1, please use row percentages instead of column percentages. That will give some idea about the prevalence of each weight categories.

Response: Thank you for the suggestion. We have updated table 1 to include row percentages, and edited the results section accordingly

6. In table 2 and 3, please report the unadjusted odds for male and female.

The unadjusted ORs for male and female participants were added to table 2 and 3.

7. In Page 14, what is the title of the table?

Response: Thank you for your comment. The title was blended with the text of the manuscript. We have updated the text to make sure that the title for the table is clearly visible.

8. In Table 3, why significant association was found for females but not for males? Please discuss.

Response: Although the association was only significant in females, the estimates were not very different, and the confidence intervals were overlapping, indicating a minor difference in the association between males and females. These findings are consistent with some previous studies, including studies in adults. More details were added to the discussion section.

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PLoS One. doi: 10.1371/journal.pone.0278490.r003

Decision Letter 1

Yih-Kuen Jan

25 Oct 2022

PONE-D-22-15444R1Association between screen time and obesity in US adolescents: A cross-sectional analysis using National Survey of Children’s Health 2016-2017PLOS ONE

Dear Dr. Bakour,

Please submit your revised manuscript by Dec 09 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Yih-Kuen Jan, PhD

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: The manuscript is much improved following the revisions you have made. I only have a few minor points:

1. in the introduction, you don't tell the reader what screen-time is (line 64) or what the recommendations are that you are referring to (line 67).

2. What are some reasons for these differences (line 99)?

3. Line 133-134, were these categories response options or did you create them? I wondered why you chose to categorise.

**********

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Reviewer #1: No

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PLoS One. 2022 Dec 1;17(12):e0278490. doi: 10.1371/journal.pone.0278490.r004

Author response to Decision Letter 1

6 Nov 2022

Reviewers' comments:

1. in the introduction, you don't tell the reader what screen-time is (line 64) or what the recommendations are that you are referring to (line 67).

Response: We have added a definition of screen time (line 58) and details regarding the recommended screen time per day for adolescents (line 62)

2. What are some reasons for these differences (line 99)?

Response: The studies that found no association between computer use and BMI were conducted in different countries (Germany, Finland, Brazil), while the study that found an association was conducted in the US. A clarification was added to the manuscript (lines 86-88)

3. Line 133-134, were these categories response options or did you create them? I wondered why you chose to categorise.

Response: The two screen time questions in the NSCH had response options of none, less than one hour, one hour, 2-3 hours, 4 hours or more. We combined the first two categories into: None or less than 1 hour, and the middle two categories into 1-3 hours, to preserve sample size, especially when stratifying by sex and physical activity/sleep, and to facilitate presentation of results (including interaction) and interpretation of findings. Clarification was added on line 120.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0278490.r005

Decision Letter 2

Yih-Kuen Jan

17 Nov 2022

Association between screen time and obesity in US adolescents: A cross-sectional analysis using National Survey of Children’s Health 2016-2017

PONE-D-22-15444R2

Dear Dr. Bakour,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Yih-Kuen Jan, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0278490.r006

Acceptance letter

Yih-Kuen Jan

21 Nov 2022

PONE-D-22-15444R2

Association between screen time and obesity in US adolescents: A cross-sectional analysis using National Survey of Children’s Health 2016-2017

Dear Dr. Bakour:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Yih-Kuen Jan

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Results of logistic regression analysis of the effect modification by sleep duration on the association between time spent watching TV or playing video games and overweight.

(DOCX)

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Attachment

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Data Availability Statement

[pone.0278490.ref001] 1.World Health Organization. Commission on Ending Childhood Obesity [Internet]. WHO. World Health Organization; [cited 2020. Nov 15]. Available from: http://www.who.int/end-childhood-obesity/en/ [Google Scholar]

[pone.0278490.ref002] 2.Centers for Disease Control and Prevention. Childhood Obesity Facts [Internet]. Overweight and Obesity. 2022. [cited 2022 Aug 31]. Available from: https://www.cdc.gov/obesity/data/childhood.html [Google Scholar]

[pone.0278490.ref003] 3.Bryan S, Afful J, Carroll M, Te-Ching C, Orlando D, Fink S, et al. National Health and Nutrition Examination Survey 2017–March 2020 Pre-pandemic Data Files [Internet]. National Center for Health Statistics (U.S.); 2021 Jun [cited 2022 Aug 31]. Available from: https://stacks.cdc.gov/view/cdc/106273 [Google Scholar]

[pone.0278490.ref004] 4.Abarca-Gómez L, Abdeen ZA, Hamid ZA, Abu-Rmeileh NM, Acosta-Cazares B, Acuin C, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. The Lancet [Internet]. 2017 Dec [cited 2020 Nov 15];390(10113):2627–42. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0140673617321293 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref005] 5.Centers for Disease Control and Prevention. Causes and Consequences of Childhood Obesity [Internet]. Centers for Disease Control and Prevention. 2020. [cited 2020 Nov 16]. Available from: https://www.cdc.gov/obesity/childhood/causes.html [Google Scholar]

[pone.0278490.ref006] 6.Cureau FV, Sparrenberger K, Bloch KV, Ekelund U, Schaan BD. Associations of multiple unhealthy lifestyle behaviors with overweight/obesity and abdominal obesity among Brazilian adolescents: A country-wide survey. Nutrition, Metabolism and Cardiovascular Diseases [Internet]. 2018. Jul [cited 2020 Nov 16];28(7):765–74. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0939475318301510 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref007] 7.Hu EY, Ramachandran S, Bhattacharya K, Nunna S. Obesity Among High School Students in the United States: Risk Factors and Their Population Attributable Fraction. Prev Chronic Dis [Internet]. 2018. Nov 8 [cited 2020 Nov 16];15:180122. Available from: http://www.cdc.gov/pcd/issues/2018/18_0122.htm doi: 10.5888/pcd15.180122 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref008] 8.Menon S, Philipneri A, Ratnasingham S, Manson H. The integrated role of multiple healthy weight behaviours on overweight and obesity among adolescents: a cross-sectional study. BMC Public Health [Internet]. 2019. Dec [cited 2020 Nov 16];19(1):1157. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-019-7007-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref009] 9.Mihrshahi S, Drayton BA, Bauman AE, Hardy LL. Associations between childhood overweight, obesity, abdominal obesity and obesogenic behaviors and practices in Australian homes. BMC Public Health [Internet]. 2018. Dec [cited 2020 Nov 16];18(1):44. Available from: http://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-017-4595-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref010] 10.Fang K, Mu M, Liu K, He Y. Screen time and childhood overweight/obesity: A systematic review and meta‐analysis. Child Care Health Dev [Internet]. 2019. Sep [cited 2020 Nov 17];45(5):744–53. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/cch.12701 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref011] 11.Friel CP, Duran AT, Shechter A, Diaz KM. U.S. Children Meeting Physical Activity, Screen Time, and Sleep Guidelines. Am J Prev Med. 2020. Oct;59(4):513–21. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref012] 12.Anderson M, Jiang J. Teens, Social Media and Technology 2018 [Internet]. Pew Research Center: Internet, Science & Tech. 2018. [cited 2022 Apr 13]. Available from: https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/ [Google Scholar]

[pone.0278490.ref013] 13.Marshall SJ, Biddle SJH, Gorely T, Cameron N, Murdey I. Relationships between media use, body fatness and physical activity in children and youth: A meta-analysis. International Journal of Obesity. 2004. Oct;28(10):1238–46. doi: 10.1038/sj.ijo.0802706 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref014] 14.Castro JAC, Nunes HEG, Silva DAS. Prevalence of abdominal obesity in adolescents: association between sociodemographic factors and lifestyle. Revista Paulista de Pediatria (English Edition) [Internet]. 2016. Sep [cited 2020 Nov 18];34(3):343–51. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2359348216000166 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref015] 15.Christofaro DGD, Andrade SMD, Mesas E, Fernandes RA, Júnior JCF. Higher screen time is associated with overweight, poor dietary habits and physical inactivity in Brazilian adolescents, mainly among girls.: 10. doi: 10.1080/17461391.2015.1068868 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref016] 16.Lajous M, Chavarro J, Peterson KE, Hernández-Prado B, Cruz-Valdéz A, Hernández-Ávila M, et al. Screen time and adiposity in adolescents in Mexico. Public Health Nutr [Internet]. 2009. Oct [cited 2020 Nov 18];12(10):1938–45. Available from: https://www.cambridge.org/core/product/identifier/S1368980009004881/type/journal_article doi: 10.1017/S1368980009004881 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref017] 17.Lee S, Wong J, Shanita S, Ismail M, Deurenberg P, Poh B. Daily Physical Activity and Screen Time, but Not Other Sedentary Activities, Are Associated with Measures of Obesity during Childhood. IJERPH [Internet]. 2014. Dec 23 [cited 2020 Nov 18];12(1):146–61. Available from: http://www.mdpi.com/1660-4601/12/1/146 doi: 10.3390/ijerph120100146 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref018] 18.Maher C, Olds TS, Eisenmann JC, Dollman J. Screen time is more strongly associated than physical activity with overweight and obesity in 9- to 16-year-old Australians: Overweight in adolescents—screen time or physical activity? Acta Paediatrica [Internet]. 2012. Nov [cited 2020 Nov 18];101(11):1170–4. Available from: http://doi.wiley.com/10.1111/j.1651-2227.2012.02804.x [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref019] 19.Phan TLT, Tucker JM, Siegel R, Christison AL, Stratbucker W, Werk LN, et al. Electronic Gaming Characteristics Associated with Class 3 Severe Obesity in Youth Who Attend the Pediatric Weight Management Programs of the COMPASS Network. Childhood Obesity [Internet]. 2019. Jan [cited 2020 Nov 16];15(1):21–30. Available from: https://www.liebertpub.com/doi/10.1089/chi.2018.0156 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref020] 20.Phuong Van Ngoc Nguyen, Tang Kim Hong, Dung The Nguyen, Annie R Robert. Excessive screen viewing time by adolescents and body fatness in a developing country: Vietnam. Asia Pacific Journal of Clinical Nutrition. 2016. Jan 1;25(1). [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref021] 21.Boone JE, Gordon-Larsen P, Adair LS, Popkin BM. Screen time and physical activity during adolescence: longitudinal effects on obesity in young adulthood. Int J Behav Nutr Phys Act [Internet]. 2007. [cited 2020 Nov 16];4(1):26. Available from: http://ijbnpa.biomedcentral.com/articles/10.1186/1479-5868-4-26 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref022] 22.Mitchell JA, Rodriguez D, Schmitz KH, Audrain-McGovern J. Greater screen time is associated with adolescent obesity: A longitudinal study of the BMI distribution from Ages 14 to 18: Screen Time and BMI from Ages 14 to 18. Obesity [Internet]. 2013. Mar [cited 2020 Nov 18];21(3):572–5. Available from: http://doi.wiley.com/10.1002/oby.20157 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref023] 23.Hands BP, Chivers PT, Parker HE, Beilin L, Kendall G, Larkin D. The associations between physical activity, screen time and weight from 6 to 14 yrs: The Raine Study. Journal of Science and Medicine in Sport [Internet]. 2011. Sep [cited 2020 Nov 16];14(5):397–403. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1440244011000739 doi: 10.1016/j.jsams.2011.03.011 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref024] 24.NSCH Survey Methodology—Data Resource Center for Child and Adolescent Health [Internet]. [cited 2021. Mar 24]. Available from: https://www.childhealthdata.org/learn-about-the-nsch/methods [Google Scholar]

[pone.0278490.ref025] 25.Paruthi S, Brooks LJ, D ‘Ambrosio Carolyn, Hall WA, Kotagal S, Lloyd RM, et al. Recommended Amount of Sleep for Pediatric Populations: A Consensus Statement of the American Academy of Sleep Medicine. Journal of Clinical Sleep Medicine. 12(06):785–6. doi: 10.5664/jcsm.5866 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref026] 26.Physical Activity Guidelines for Americans, 2nd edition.: 118. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref027] 27.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. Nov;140(Suppl 2):S97–101. doi: 10.1542/peds.2016-1758K [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref028] 28.Zhu Z, Tang Y, Zhuang J, Liu Y, Wu X, Cai Y, et al. Physical activity, screen viewing time, and overweight/obesity among Chinese children and adolescents: An update from the 2017 physical activity and fitness in China—The youth study. BMC Public Health. 2019. Feb;19(1). doi: 10.1186/s12889-019-6515-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref029] 29.Te Velde SJ, De Bourdeaudhuij I, Thorsdottir I, Rasmussen M, Hagströmer M, Klepp KI, et al. Patterns in sedentary and exercise behaviors and associations with overweight in 9-14-year-old boys and girls—A cross-sectional study. BMC Public Health. 2007. Dec;7(1):16–16. doi: 10.1186/1471-2458-7-16 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref030] 30.Rey-López JP, Vicente-Rodríguez G, Biosca M, Moreno LA. Sedentary behaviour and obesity development in children and adolescents. Nutrition, Metabolism and Cardiovascular Diseases. 2008. Mar;18(3):242–51. doi: 10.1016/j.numecd.2007.07.008 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref031] 31.Kautiainen S, Koivusilta L, Lintonen T, Virtanen SM, Rimpelä A. Use of information and communication technology and prevalence of overweight and obesity among adolescents. International Journal of Obesity. 2005. Aug;29(8):925–33. doi: 10.1038/sj.ijo.0802994 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref032] 32.Raptou E, Papastefanou G, Mattas K. Investigating the influence of eating habits, body weight and television programme preferences on television viewing time and domestic computer usage. Perspect Public Health. 2017. Jan 1;137(1):59–72. doi: 10.1177/1757913916636925 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref033] 33.Ld D, Wr T, Das S, Fcs G, J M, Dgd C. Food advertisements on television and eating habits in adolescents: a school-based study. Revista de saude publica [Internet]. 2020. [cited 2022 Apr 13];54. Available from: https://pubmed.ncbi.nlm.nih.gov/32491114/ [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref034] 34.Utter J, Neumark-Sztainer D, Jeffery R, Story M. Couch potatoes or French fries: Are sedentary behaviors associated with body mass index, physical activity, and dietary behaviors among adolescents? Journal of the American Dietetic Association. 2003. Oct;103(10):1298–305. doi: 10.1016/s0002-8223(03)01079-4 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref035] 35.Sampasa-Kanyinga H, Colman I, Goldfield GS, Hamilton HA, Chaput JP. Sex differences in the relationship between social media use, short sleep duration, and body mass index among adolescents. Sleep Health. 2020. Oct;6(5):601–8. doi: 10.1016/j.sleh.2020.01.017 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref036] 36.Dunstan DW, Salmon J, Owen N, Armstrong T, Zimmet PZ, Welborn TA, et al. Associations of TV viewing and physical activity with the metabolic syndrome in Australian adults. Diabetologia. 2005. Nov 1;48(11):2254–61. doi: 10.1007/s00125-005-1963-4 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref037] 37.Bertrais S, Beyeme-Ondoua JP, Czernichow S, Galan P, Hercberg S, Oppert JM. Sedentary Behaviors, Physical Activity, and Metabolic Syndrome in Middle-aged French Subjects. Obesity Research. 2005;13(5):936–44. doi: 10.1038/oby.2005.108 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref038] 38.Mutunga M, Gallagher AM, Boreham C, Watkins DC, Murray LJ, Cran G, et al. Socioeconomic differences in risk factors for obesity in adolescents in Northern Ireland. International Journal of Pediatric Obesity. 2006. Jan 1;1(2):114–9. doi: 10.1080/17477160600569560 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref039] 39.Crowe M, Sampasa-Kanyinga H, Saunders TJ, Hamilton HA, Benchimol EI, Chaput JP. Combinations of physical activity and screen time recommendations and their association with overweight/obesity in adolescents. Can J Public Health. 2020. Aug 1;111(4):515–22. doi: 10.17269/s41997-020-00313-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref040] 40.Tudor-Locke C, Craig CL, Cameron C, Griffiths JM. Canadian children’s and youth’s pedometer-determined steps/day, parent-reported TV watching time, and overweight/obesity: The CANPLAY Surveillance Study. Int J Behav Nutr Phys Act. 2011. Jun 25;8:66. doi: 10.1186/1479-5868-8-66 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278490.ref041] 41.Brault MC, Turcotte O, Aimé A, Côté M, Bégin C. Body Mass Index Accuracy in Preadolescents: Can We Trust Self-Report or Should We Seek Parent Report? J Pediatr. 2015. Aug;167(2):366–71. doi: 10.1016/j.jpeds.2015.04.043 [DOI] [PubMed] [Google Scholar]

[pone.0278490.ref042] 42.Goodman E, Hinden BR, Khandelwal S. Accuracy of teen and parental reports of obesity and body mass index. Pediatrics. 2000. Jul;106(1 Pt 1):52–8. doi: 10.1542/peds.106.1.52 [DOI] [PubMed] [Google Scholar]

PERMALINK

Association between screen time and obesity in US adolescents: A cross-sectional analysis using National Survey of Children’s Health 2016–2017

Chighaf Bakour

Fahad Mansuri

Courtney Johns-Rejano

Michelle Crozier

Ronee Wilson

William Sappenfield

Roles

Abstract

Objective

Methods

Results

Conclusion

Introduction

Methods

Measures

Outcome variable

Exposure variable

Effect modifiers

Statistical analysis

Results

Table 1. Characteristics of adolescent participants in the National Survey of Children’s Health.

Table 2. Results of logistic regression analysis of the association between time spent watching TV or playing video games and BMI in adolescents.

Table 3. Results of logistic regression analysis of the association between time spent with computers, cell phones, handheld video games and other electronic devices, and BMI in adolescents.

Table 4. Results of logistic regression analysis of the effect modification by physical activity on the association between time spent watching TV or playing video games and overweight.

Table 5. Results of logistic regression analysis of the effect modification by physical activity on the association between time spent using a computer or handheld device and overweight.

Discussion

Conclusion

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Yih-Kuen Jan

Roles

Author response to Decision Letter 0

Decision Letter 1

Yih-Kuen Jan

Roles

Author response to Decision Letter 1

Decision Letter 2

Yih-Kuen Jan

Roles

Acceptance letter

Yih-Kuen Jan

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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