Examining communication- and media-based recreational sedentary behaviors among Canadian youth: Results from the COMPASS study

Preventive Medicine 74 (2015) 74–80

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Examining communication- and media-based recreational sedentary behaviors among Canadian youth: Results from the COMPASS study Scott T. Leatherdale ⁎, Amanda Harvey School of Public Health and Health Systems, University of Waterloo, 200 University Avenue, Waterloo, ON, Canada, N2L 3G1

a r t i c l e

i n f o

Available online 28 February 2015 Keywords: Screen time Sedentary behavior Obesity Physical activity Tobacco Alcohol Marijuana Youth

a b s t r a c t Objectives. To examine the prevalence of different communication- and media-based sedentary behaviors and examine how they are associated with modifiable risk behaviors and key demographic correlates among a large sample of youth. Methods. Data from 23,031 grade 9 to grade 12 students in Year 1 (2012–2013) of the COMPASS study (Canada) were used to examine the prevalence of sedentary behaviors by gender and by grade. The betweenschool variance in sedentary behaviors was calculated and models were developed to examine how modifiable risk factors and demographic correlates were associated with sedentary behaviors. Results. Youth averaged 494 (±313) min/day of sedentary behavior and 96.7% of the sample exceeded the sedentary behavior guidelines of no more than 2 h per day. Significant between-school random variation in the sedentary behaviors was identified. Substance use, weight status, ethnicity, and gender were the main predictors of the sedentary behaviors examined. Conclusions. The vast majority of youth in the COMPASS sample are considered highly sedentary. The evidence clearly suggests we need to develop more effective methods of intervening, that school-based programming is warranted, and that gender-specific programming may be required. © 2015 Elsevier Inc. All rights reserved.

Introduction Many forms of communication- and media-based behaviors that are common among youth (e.g., watching television, playing passive video games, talking on the phone or texting, playing on the computer, etc.) are considered to be sedentary behaviors. Research has demonstrated that these types of sedentary behaviors tend to be established during adolescence (Leatherdale and Rynard, 2013; Leatherdale and Ahmed, 2011), and there is now evidence that these sedentary behaviors are linked to increased risk of future chronic disease morbidity and mortality (Tremblay et al., 2010). Given the negative health implications of sedentary behavior, the Canadian Society for Exercise Physiology CSEP developed the world's first evidence-based sedentary behavior guidelines (Tremblay et al., 2011); youth should spend no more than 2 h per day in recreational screen time and should limit the time spent on other activities that involve little to no physical movement (CSEP, 2014a). Most Canadian youth (89.4%) report exceeding this recommendation (Leatherdale and Rynard, 2013), with additional evidence suggesting that in fact Canadian youth spend on average 7 to 8 h per day in such recreational sedentary behaviors (Colley et al., 2013; Leatherdale and Ahmed, 2011). Further examination of the different ⁎ Corresponding author. Fax: +1 519 746 2510. E-mail address: [email protected] (S.T. Leatherdale).

http://dx.doi.org/10.1016/j.ypmed.2015.02.005 0091-7435/© 2015 Elsevier Inc. All rights reserved.

types of sedentary behaviors among youth populations seems warranted, especially considering these behaviors are amenable to modification. Previous research has examined both the total amount of time youth spend in recreational sedentary behavior (Leatherdale and Ahmed, 2011; Leatherdale and Rynard, 2013; Saunders et al., 2014) and the time spent in specific recreational sedentary pursuits (Leatherdale and Ahmed, 2011; Leatherdale et al., 2010; Liwander et al., 2013; Saunders et al., 2014). For instance, research has identified that the time spent in different types of sedentary behavior can vary by gender (e.g., males are more apt to spend time playing video games) and by grade (e.g., time spent watching TV declines as grade increases) (Leatherdale and Ahmed, 2011; Leatherdale et al., 2010). It can also vary by ethnicity (e.g., non-White youth tend to have higher screen time than white youth), and by weekly spending money (e.g., youth with no disposable income have higher TV and video game time, but lower computer use time) (Haug et al., 2009; Leatherdale and Ahmed, 2011; Leatherdale et al., 2010). Given the ongoing emergence of new sedentary behaviors among youth populations (e.g., text messaging), and that research often examines recreational sedentary behavior as a total measure of screen time (e.g., Leatherdale and Rynard, 2013), it would be informative to understand how much time youth spend in different recreational sedentary pursuits and how different nonmodifiable demographic correlates are associated with the different types of recreational sedentary behaviors.

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Substance use (smoking, heavy drinking, drug use), obesity, and the correlates of obesity (poor diet and physical inactivity) tend to be established during adolescence with most Canadian youth exhibiting one or more of these modifiable risk factors for future chronic disease related morbidity and mortality (Leatherdale and Rynard, 2013). Recent research has identified that many of these modifiable risk factors appear to be associated with sedentary behavior. For instance, among youth there is evidence of a link between sedentary behavior and smoking (de la Haye et al., 2014; Leatherdale and Ahmed, 2011), binge drinking (Busch et al., 2013; de la Haye et al., 2014), and marijuana use (de la Haye et al., 2014). Sedentary behavior also appears associated with being overweight or obese (Costigan et al., 2013; Mitchell et al., 2013; Sigmundova et al., 2014; Thibault et al., 2013), physical inactivity (Busch et al., 2013; Costigan et al., 2013; Pearson et al., 2014), and poor eating habits such as inadequate fruit and vegetable consumption (Busch et al., 2013; Pearson and Biddle, 2011; Sigmundova et al., 2014). Research also suggests that recreational sedentary behavior is associated with multiple risk behaviors in youth (Carson et al., 2011; Costigan et al., 2013). Moving forward, it would be informative to examine the associations between different recreational sedentary behaviors and engagement in multiple risk behaviors more comprehensively within one study. As such, the objective of this study is to examine the prevalence of different communication- and media-based recreational sedentary behaviors and examine how they are associated with major modifiable risk factors for chronic disease and key demographic correlates among a large sample of youth in Year 1 of the COMPASS study. Methods Design COMPASS is a cohort study designed to collect longitudinal data from a sample of grades 9 to 12 secondary school students in Ontario and Alberta, Canada (Leatherdale et al., 2014a). The current paper reports findings from the Year 1 data collection, conducted during the 2012/2013 school year. A full description of the study methods is available in print (Leatherdale et al., 2014a) or online (www.compass.uwaterloo.ca). Participants In Year 1, 43 Ontario schools were purposefully recruited because they included a student population of at least 100 students per grade and permission to use active-information passive-consent parental permission protocols. As such, this sample is not representative of the Ontario or Canadian student population. In the active-information passive-consent protocol, the parent(s) or guardian(s) of eligible students were mailed an information letter about the COMPASS study and were asked to contact the COMPASS recruitment coordinator using either the toll-free phone number or email address provided in the information letter, should they not want their child to participate. All eligible students whose parent(s) or guardian(s) did not contact the COMPASS team to withdraw their child were deemed eligible to participate. Students could decline to participate at any time. A total of 30,147 students were enrolled in the 43 COMPASS secondary schools in Year 1. Overall, 80.2% (n = 24,173) of eligible students completed the COMPASS student questionnaire in class time on the day of their schools scheduled data collection. Missing respondents resulted from absenteeism/classroom spares on the day and time of the survey (18.8%), student refusal (0.1%), and parental refusal (0.9%). An additional 1142 students were deleted due to missing data for gender and one or more of the sedentary behavior measures resulting in a final sample of 23,031 respondents for this manuscript. Measures The operational definitions for the measures used in this manuscript are consistent with previous research based on national standards or current national public health guidelines (Leatherdale et al., 2014b; Leatherdale and Laxer, 2013; Leatherdale and Rynard, 2013; Wong et al., 2012).

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Communication- and media-based recreational sedentary behaviors (sedentary behavior) Using a previously validated measure of self-reported sedentary behaviors appropriate for use in large population-level surveys (Leatherdale et al., 2014b), the COMPASS questionnaire asked respondents to report the average time in minutes per day (min/day; measured in 15 min intervals) that they spent: “Watching/streaming TV shows or movies”; “Playing video/computer games”; “Talking on the phone”; “Surfing the internet”; and, “Texting, messaging, emailing”. We then calculated a conservative estimate of the total sedentary behavior (TSB) time per day on recreational behaviors based on the sum of lowest values for each of the five different recreational sedentary behaviors reported. Consistent with the CSEP guidelines for youth (CSEP, 2014a; Tremblay et al., 2011), respondents were classified as highly sedentary if their calculated TSB was more than 2 h a day. As outlined elsewhere (Leatherdale et al., 2014b), the COMPASS questionnaire also asked respondents to report the average time in minutes per day spent doing homework and sleeping. Given that these sedentary behaviors are not considered recreational, they were not examined within this manuscript. Modifiable risk factor correlates Consistent with previously validated measures of current smoking (Wong et al., 2012), students who reported ever smoking 100 cigarettes and any smoking in the previous 30 days were classified as current smokers. The measures for current binge drinking and marijuana use were consistent with national standards used in previous research (Leatherdale and Rynard, 2013). Those who reported binge drinking (five or more drinks on one occasion) once a month or more were classified as current binge drinkers, and those who reported marijuana use once a month or more were classified as current marijuana users. While the Canadian low-risk drinking guidelines highlight that for females, four or more drinks in a day would constitute binge drinking (Butt et al., 2011), we are not able to define binge drinking for females this way within this manuscript due to the wording of the binge drinking measure used in COMPASS (only asks about five or more drinks on one occasion). As such, our measure of binge drinking as it relates to female participants is likely underestimated compared to if a threshold of four drinks were available for females. Using previously validated measures of self-reported height and weight (Leatherdale and Laxer, 2013), body mass index (BMI) was calculated for each student using the measures of weight (kg) and height (m) (BMI kg/m2). Weight status was then determined using the BMI classification system of the World Health Organization (WHO, 2007) based on age and sex adjusted BMI cutpoints. Using previously validated measures (Leatherdale et al., 2014b), physical activity was measured by asking respondents how many minutes of hard and moderate activity they engaged in for each of the last 7 days. Consistent with the Canadian physical activity guidelines for youth (CSEP, 2014b), respondents who did not report performing 60 min of moderate-to-hard activity daily and hard activity on at least three out of the last 7 days were classified as being inactive. Using previously validated measures of eating behavior (Leatherdale and Laxer, 2013), respondents were asked to report how many servings of fruits and/or vegetables they had on a usual day. Consistent with the Canada Food Guide recommendations for fruit and vegetable consumption among teens (Health Canada, 2014), males who reported less than eight servings per day and females who reported less than seven servings per day were classified as having inadequate fruit and vegetable consumption. Demographic correlate measures Correlates included grade (9, 10, 11, 12), gender (male, female), and ethnicity (recoded as White, off-reserve Aboriginal, Other). Age was not included in the analysis due to the high correlation with grade and because grade is a more meaningful indicator for school-based prevention planning. Weekly spending money was assessed by asking respondents to report how much money they usually get each week to spend on themselves or save (recoded as $0, $1 to $20, $21 to $100, more than $100, I don't know). Analyses Descriptive analyses of TSB and the five recreational sedentary behaviors were examined by gender and by grade. Descriptive analyses of the modifiable risk factors and demographic correlates were examined by gender. Six different mixed linear models were used to calculate the between-school variance for TSB, and time spent watching TV or videos, playing video or computer games, talking on the phone, surfing the internet, and texting, messaging and emailing. Six generalized linear models, each with school as a class statement, were used

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to examine how the modifiable risk factors and demographic correlates were associated with TSB, and time spent watching TV or videos, playing video or computer games, talking on the phone, surfing the internet, and texting, messaging and emailing. The statistical package SAS 9.3 was used for all analyses.

Results Data were available for 23,031 students in the 43 COMPASS Year 1 schools. The sample was 50.5% (n = 11,633) male and 49.5% (n = 11,398) female. The majority of the sample (96.7%, n = 21,942) were classified as exceeding the sedentary behavior guideline recommendation. Our conservative estimate of TSB was 494 (±313) min/day, and the average time per day spent watching TV or movies was 117 (± 85) min/day, playing video games or computer games was 82

(±108) min/day, talking on the phone was 31 (±61) min/day, surfing the internet was 127 (± 113) min/day, and texting, messaging, or emailing was 137 (±148) min/day. The gender specific distributions are presented in Table 1. The TSB was similar between males [495.4 (± 311.8) min/day] and females [493.5 (± 314.0) min/day], although males were more likely than females to exceed the sedentary behavior guideline recommendation (p b 0.01). Males spent more time playing video or computer games (p b 0.001) than females. Females spent more time talking on the phone (p b 0.001), surfing the internet (p b 0.001), and texting, messaging, or emailing (p b 0.001) than males. Time spent watching TV or movies did not vary by gender (p = 0.32). The prevalence rates of the modifiable correlates as shown in Table 1 are consistent with prevalence estimates from a nationally representative sample of Canadian youth (Leatherdale and Rynard, 2013).

Table 1 Descriptive statistics for the communication- and media-based recreational sedentary behaviors, modifiable health behaviors, and demographic correlates in the COMPASS Year 1 sample by gender (2012–2013), Ontario, Canada.

Sedentary behaviors (min/day) Total Sedentary Behavior Watching TV or movies Playing video or computer games Talking on the phone Surfing the internet Texting, messaging, emailing

Male

Female

(n = 11,633)

(n = 11,398)

Mean (±SD)

Mean (±SD)

t-Test

495.4 (311.8) 116.5 (86.8) 123.4 (117.1) 24.9 (56.7) 116.8 (109.1) 113.8 (135.2)

493.5 (314.0) 117.6 (84.1) 40.4 (79.3) 38.2 (64.4) 137.2 (116.8) 160.2 (156.0)

p = 0.65 p = 0.32 p b 0.001 p b 0.001 p b 0.001 p b 0.001

% (n) Sedentary behavior guideline Exceeded recommendation Meets recommendation Current smoker (cigarettes only) Yes No Current binge drinker Yes No Current marijuana user Yes No Weight status b Underweight Healthy weight Overweight Obese Data missing Physical activity Active Inactive Fruit and vegetable consumption c Adequate Inadequate Grade 9 10 11 12 Ethnicity White Off-reserve Aboriginal Other Weekly spending money None $1 to $20 $21 to $100 More than $100 I don't know a b c

a

% (n)

a

Chi-square

97.1 (11131) 2.9 370

96.3 (10811) 3.7 (418)

χ2 = 10.84,df = 1,p b 0.01

7.0 (810) 93.0 (10823)

3.8 (429) 96.2 (10969)

χ2 = 115.80,df = 1,p b 0.001

25.0 (2913) 75.0 (8720)

20.7 (2360) 79.3 (9038)

χ2 = 61.30,df = 1,p b 0.001

19.8 (2297) 80.2 (9336)

13.0 (1478) 87.0 (9920)

χ2 = 193.02,df = 1,p b 0.001

1.5 (170) 53.4 (6214) 16.4 (1912) 8.8 (1020) 19.9 (2317)

1.4 (158) 61.5 (7018) 11.2 (1273) 3.6 (407) 22.3 (2542)

χ2 = 444.89,df = 4,p b 0.001

54.2 (6305) 45.8 (5328)

39.5 (4502) 60.5 (6896)

χ2 = 499.59,df = 1,p b 0.001

4.1 (479) 95.9 (11154)

5.6 (637) 94.4 (10761)

χ2 = 27.02,df = 1,p b 0.001

26.1 (3031) 25.1 (2927) 24.7 (2873) 24.1 (2802)

26.4 (3014) 25.9 (2957) 24.4 (2774) 23.3 (2653)

χ2 = 3.61,df = 3,p = 0.307

70.5 (8162) 4.7 (540) 24.8 (2876)

73.2 (8323) 4.8 (544) 22.0 (2496)

χ2 = 26.45,df = 2,p b 0.001

16.3 (1901) 30.6 (3555) 25.3 (2943) 15.5 (1806) 12.3 (1428)

15.0 (1710) 30.7 (3495) 28.7 (3269) 12.3 (1407) 13.3 (1517)

χ2 = 77.57,df = 4,p b 0.001

Numbers may not add to total because of missing values. Body mass index (BMI) values used to determine weight status have been adjusted for age and gender. ≥7 servings for females, ≥8 servings for males.

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Average number of minutes per day (min/day)

S.T. Leatherdale, A. Harvey / Preventive Medicine 74 (2015) 74–80

G ra d e 9

G ra d e 10

G ra d e 11

G ra d e 12

Fig. 1. Prevalence of communication- and media-based recreational sedentary behaviors by grade. COMPASS, Year 1 (20012–2013), Ontario, Canada.

Fig. 1 displays the recreational sedentary behaviors by grade. As shown, aside from the time spent playing video or computer games (20% relative decrease between grades 9 and 12) or surfing the internet (15% relative increase between grades 9 and 12), there was very little consistent or meaningful change in the time spent in the other types of sedentary behavior across grade. Significant between-school random variation (p b 0.001) was identified for TSB, watching TV or videos, playing video or computer games, talking on the phone, surfing the internet, and texting, messaging, or emailing; school-level differences accounted for 1.6% of the variability in TSB, 1.6% of the variability in the time spent watching TV and videos, 1.4% of the variability in the time spent playing video or computer games, 1.4% of the variability in the time spent talking on the phone, 2.2% of the variability in the time spent surfing the internet, and 1.0% of the variability in the time spent texting, messaging, or emailing. Table 2 presents the results of the six generalized linear models examining TSB and the five communication- and media-based behaviors individually.1 Modifiable risk factors As shown in Table 2, compared to students who are not current smokers, there was a large increase in TSB (β 70.3), and modest increases in time spent talking on the phone (β 15.3) or texting, messaging, or emailing (β 20.7) among current smokers. Compared to students who are not current binge drinkers, there was a large increase in TSB (β 42.0) and time spent texting, messaging, or emailing (β 47.4) among current binge drinkers. Compared to students who are not current marijuana users, there was a large increase in TSB (β 89.3) 1 The significant associations with effect sizes larger than 15 min are presented here, all other associations are available in Table 2.

and time spent texting, messaging or emailing (β 40.1), and a modest increase in time spent surfing the internet (β 21.0) among current marijuana users. Compared to normal weight students, there were large increases in TSB (β 81.3) and time spent playing video and computer games (β 36.3), and a modest increase in time spent surfing the internet (β 25.7) among overweight students, and a modest increase in TSB (β 27.4) among obese students. Compared to students who are physically active, there were modest increases in TSB (β 28.6) and time spent texting, messaging, or emailing (β 27.9) among physically inactive students.

Demographic correlates As shown in Table 2, compared to females, there was a large increase in time spent playing video or computer games (β 83.1), a large decrease in time spent texting, messaging, or emailing (β -55.3), and modest declines in time spent talking on the phone (β − 15.9) and surfing the internet (β −23.2) among males. Compared to grade 9 students, there was a modest decline in time spent texting, messaging, or emailing among grade 12 students (β −16.8), and a modest increase in time spent surfing the internet among grade 12 students (β 19.0). Compared to White students, there was a large increase in TSB among off-reserve Aboriginal (β 37.9) and other than White (β 70.0) students, a large increase in time spent surfing the internet among other than White students (β 37.9), modest increases in time spent playing video or computer games (β 25.5) and surfing the internet (β 15.7) among off-reserve Aboriginal students, and a modest increase in time spent talking on the phone (β 15.3) among other than White students. Compared to students with $0 weekly spending money, there was a modest increase in TSB among students with $1 to $20 (β 18.5), modest to large increases in time spent texting, messaging, or emailing among students with $21 to $100 (β 26.8) or more than $100 (β 37.3), and a

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Table 2 Generalized linear models examining the associations between the communication- and media-based recreational sedentary behaviors and modifiable health behaviors and demographic correlates in the COMPASS Year 1 sample (2012–2013), Ontario, Canada. β (SE)

Gender Male Grade 10 11 12 Ethnicity Off-reserve Aboriginal Other Weekly spending money $1 to $20 $21 to $100 More than $100 I don't know Current smoker (cigarettes only) Yes Current binge drinker Yes Current marijuana user Yes Weight status a Overweight Obese Data missing Physical activity Inactive Fruit and vegetable consumptionb Adequate

Total SB

TV

−14.7 (4.1)***

−3.5 (1.1)*

9.9 (5.6) −0.7 (5.8) −9.1 (6.1) 37.9 (9.1)*** 70.0 (4.8)*** 18.5 (6.2)* 13.2 (6.5)* 10.1 (7.7) 1.5 (7.6)

1.0(1.6) −0.5 (1.6) 0.3 (1.7) 2.0 (2.5) 7.2 (1.3)*** 4.4 (2.7) −2.0 (1.8) −6.6 (4.2) −1.7 (2.1)

Games 83.1 (1.3)*** −3.2 (1.8) −9.4 (1.9)*** −10.0 (2.0)***

Phone

Internet

Text

−15.9 (0.8)***

−23.2 (1.5)***

−55.3 (1.9)***

12.7 (2.0)*** 13.2 (2.1)*** 19.0 (2.2)***

−1.3 (2.6) −5.3 (2.7)* −16.8 (2.8)*** −6.7 (4.2) 6.2 (2.2)**

0.7(1.1) 1.4 (1.1) −1.5 (1.2)

25.5 (2.9)*** 3.5 (1.5)*

1.5 (1.8) 15.3 (0.9)***

15.7 (3.3)*** 37.9 (1.7)***

−3.8 (2.0) −13.9 (2.1)*** −17.0 (2.5)*** −12.9 (2.4)***

6.3 (1.2)*** 8.4 (1.3)*** 9.3 (1.5)*** 7.2 (1.5)***

−1.2 (23) −6.1 (2.4)* −13.0 (2.8)*** −9.6 (2.8)***

12.8 (2.9)*** 26.8 (3.0)*** 37.3 (3.6)*** 18.5 (3.5)***

13.7 (3.1)***

15.3 (1.9)***

12.3 (3.5)***

20.7 (4.5)***

−13.0 (1.8)***

6.4 (1.1)***

70.3 (9.6)***

8.4 (2.7)**

42.0 (5.5)***

1.0 (1.5)

89.3 (6.3)***

5.1 (1.8)**

11.7 (2.0)***

11.3 (1.2)***

21.0 (2.3)***

40.1 (2.9)***

81.3 (17.0)*** 27.4 (5.2)*** 95.1 (5.2)***

8.7 (4.7) 11.8 (1.5)*** 19.1 (1.4)***

36.3 (1.7)*** 8.7 (1.7)*** 32.2 (1.7)***

7.9 (3.3)* 1.7 (1.0) 10.4 (1.0)***

25.7 (6.2)*** 8.9 (1.9)*** 24.8 (1.9)***

2.8 (7.9) −3.7 (2.4) 8.7 (2.4)***

28.6 (4.1)***

4.8 (1.1)***

−7.2 (1.3)***

6.3 (0.8)***

−3.1 (1.5)*

−1.4 (3.0)

9.2 (1.8)***

−5.4 (3.4)

1.3 (9.4)

−9.4 (2.6)***

0.2 (2.0)

47.4 (2.6)***

27.9 (1.9)*** 8.2 (4.3)

β denotes the variable estimate, SE denotes the standard error of the variable estimate. Total SB, total sedentary behavior; TV, watching TV or movies; Games, playing video or computer games; Phone, talking on the phone; Internet, surfing the internet; Text, texting, messaging, emailing. *p b 0.05 **p b 0.01 ***p b 0.001 a Body mass index (BMI) values used to determine weight status have been adjusted for age and gender. b ≥7 servings for females, ≥8 servings for males.

modest decline in time spent playing video or computer games among students with more than $100 (β −17.0). Discussion These findings clearly demonstrate that many youth who participated in Year 1 of the COMPASS study are participating in over 8 h a day of communication- and media-based recreational sedentary behaviors. This is concerning considering the CSEP guidelines recommend a maximum of 2 h a day (CSEP, 2014a; Tremblay et al., 2011). The findings from this study are supported by previous literature using nationally representative measured data (Colley et al., 2013) and selfreported data (Leatherdale and Rynard, 2013; Leatherdale and Ahmed, 2011) and clearly demonstrate that communication-based media is creating a potentially large negative health issue for youth, especially considering the physical and psychological health consequences associated with excessive sedentary behavior (Augner and Hacker, 2012; Tremblay et al., 2010). Given the large amount of time that youth spend daily in sedentary pursuits, further research into early interventions is necessary. Considering that by secondary school these recreational sedentary behaviors are already well entrenched (GordonLarsen et al., 2004), interventions may need to begin while students are in elementary school. Historically, much of the literature has examined TV and computer/ video game habits as their main outcome of interest. However, newer technologies exist as part of the sedentary behavior spectrum. Our data suggest that these newer technologies now represent the dominant proportion of the time youth spend being sedentary. Consistent with a few recent studies (Costigan et al., 2013; de la Haye et al., 2014; Leatherdale and Ahmed, 2011), youth appear to now spend

most of their recreational sedentary time surfing the internet, texting, messaging or emailing. It will be important to examine these newer technologies in more depth as they will require new and innovative strategies to diminish the time spent in these modifiable sedentary pursuits. For instance, evidence suggests that having a TV in the bedroom is highly associated with excessive TV viewing (Wethington et al., 2013) and adiposity (Chaput et al., 2014). Moving forward, it may be informative to expand this work to also examine the role of having access to the internet or mobile devices in the bedroom. By developing our understanding of when, where and how youth access the internet socially or perform texting, messaging or emailing, similar interventions can be developed (i.e., if youth spend hours texting at night, interventions to prevent youth from bringing mobile devices into their room when they go to bed could be developed). The results clearly demonstrated that there are associations between the different types of recreational sedentary behaviors and substance use (smoking, binge drinking and marijuana use). Although many of the associations identified were modest when examining individual types of recreational sedentary behavior, the effect sizes for TSB were large and highlight the importance of developing interventions to address recreational sedentary behavior given that it is associated with the likelihood of engaging in substance use (de la Haye et al., 2014). This finding suggests that it may be worth examining how changes in sedentary behavior over time impact youth substance use patterns or conversely how changes in substance use are related to sedentary behavior. Longitudinal data from the COMPASS study can be used to provide such valuable insight in the future as additional waves of data become available. Consistent with the literature (Costigan et al., 2013; Thibault et al., 2013), weight status was associated with recreational sedentary

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behavior. Being overweight appears to have the largest effect sizes where overweight youth were strongly associated with TSB, as well as time spent playing video or computer games and time spent surfing the internet. Youth with missing BMI were also more likely to be sedentary in a pattern that mimics youth who are overweight, with effect sizes even larger than youth who are obese. Given the evidence that youth with missing BMI are themselves motivated non-responders (i.e., have lower daily energy expenditure values compared to youth who report their height and weight to calculate BMI; Arbour-Nicitopoulos et al., 2010), this finding may not be surprising as it is possible that the nonresponders are themselves overweight. The amount of missing BMI data reported here is consistent with the self-reported missing BMI data from the Health Behavior in School-aged Children study (Haug et al., 2009). Regardless, these results highlight that future sedentary behavior prevention interventions may want to target overweight youth, especially considering that these youth are also at increased risk for chronic disease due to being overweight or obese. While physical activity was modestly associated with the recreational sedentary behaviors examined here, fruit and vegetable consumption was only associated with time spent watching TV. The association with physical activity is consistent with previous research (Costigan et al., 2013), and highlights that it may be possible to decrease sedentary time by increasing physical activity among youth. However, given the evidence that highly active youth can also be highly sedentary (Pearson et al., 2014; Wong and Leatherdale, 2009), sedentary behavior interventions should not only be targeted to physically inactive youth. The association between fruit and vegetable consumption and watching TV is also consistent with Sigmundova et al. (2014) but the effect size identified here is small. However, given that research has recently identified that it is possible to influence the dietary habits of children through their TV habits in the home (Olafsdottir et al., 2014), this might warrant potential consideration for researchers trying to intervene in the home context. As expected (Haug et al., 2009; Leatherdale and Ahmed, 2011), gender was a consistent predictor of recreational sedentary behavior with unique risk and protective implications depending on the type of sedentary behavior examined. This is aligned with previous research that identified females with higher TV viewing time reporting lower life satisfaction relative to boys (Moor et al., 2014). Moving forward, sedentary behavior prevention initiatives may require interventions tailored to specific genders and specific types of behaviors. Also consistent with previous research was the finding that in general youth of non-White ethnicity tended to spend more time in recreational sedentary behavior than White youth (Leatherdale et al., 2010). A unique finding was that youth with more disposable income were at increased risk for talking on the phone or texting, emailing, or messaging, but at decreased risk for playing video or computer games and surfing the internet. This is consistent with research demonstrating that youth who have jobs and work outside of school have lower levels of some forms of recreational sedentary behavior relative to their non-working peers (Silva et al., 2014). Unfortunately, the COMPASS data do not allow us to determine if the extra disposable income available to some students is used to cover the costs associated with data plans for phones or internet provider plans required to facilitate more talking on the phone or texting, emailing and messaging. Research among elementary school youth has suggested that schools are an ideal location for sedentary behavior interventions (Leatherdale et al., 2010). The results of this study further suggest that secondary schools may also provide an appropriate setting in which to intervene given that there was significant between-school variability identified for TSB and all of the other types of sedentary behavior examined. Although the variability identified may appear small (1.0–2.2%), the high prevalence of very sedentary youth and the principles of population intervention via the risk paradox outlined by Rose (1992) highlight that even a small impact across a large number of schools could be substantial at the population-level.

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Limitations The current study has several limitations common to survey research. First, the study relies on self-reports of sedentary behavior and the modifiable risk factors examined, so the findings reflect some under-reporting and missing data which is common in survey research. Although COMPASS data are based on self-reported measures, the questionnaire measures have previously demonstrated satisfactory reliability and validity (Leatherdale et al., 2014b; Leatherdale and Laxer, 2013; Wong et al., 2012) and honest reporting was encouraged by ensuring confidentiality during data collection. Moreover, with the measures available in COMPASS, it is not possible to determine if all of the time spent playing video or computer games was inactive (due to the emergence of active video games) or texting, messaging or emailing was inactive (as youth with mobile phones could be walking while communicating with others via those means). However, given the evidence that active video games generally do little to actually increase energy expenditure among youth beyond light exertion (LeBlanc et al., 2013), that there is no available evidence that describes the energy expenditure of youth who are active while texting, messaging or emailing [i.e., if the energy expenditure is below 1.5 metabolic equivalents (METS) while a student walks and texts it would still be considered sedentary (Tremblay et al., 2011)], and it is not feasible to have objective measures or participant behavior logs to more accurately measure sedentary behavior in a large population-based study the size of COMPASS. An additional limitation of the survey is that the measure of binge drinking used in COMPASS may under-report binge drinking prevalence for females compared to if gender-specific measures were used based on the low-risk drinking guidelines (Butt et al., 2011). The cross-sectional nature of these data does not allow for causal inferences regarding trends over time and no data were available to examine parental influences on the factors examined. Given that COMPASS data are longitudinal, potential bias in the self-reported data is partially mitigated within future research examining the onset or temporal relationships between recreational sedentary behavior and risk factor or demographic correlates over time. Considering that media- and communication-based behaviors are constantly evolving and becoming more common among youth populations, the very high prevalence of COMPASS youth exceeding recommended guidelines for recreational sedentary behavior identified here is cause for concern. Clearly we need to develop more effective methods of intervening, as sedentary behavior is currently dominating a large portion of recreational time among youth. Progress in reducing or limiting youth sedentary behavior will require efforts from many different stakeholders in many different contexts. The evidence presented here suggest that while school-based interventions alone will not be sufficient to solve the problem, it is unlikely that the current trends can be reversed without more effective school-based prevention programming. Conflict of Interest Statement The authors declare that they have no competing interests or conflicts of interest.

Acknowledgment The COMPASS study was supported by a bridge grant from the Canadian Institutes of Health Research (CIHR) Institute of Nutrition, Metabolism and Diabetes (INMD) through the “Obesity—Interventions to Prevent or Treat” priority funding awards (OOP-110788; grant awarded to S.T. Leatherdale) and an operating grant from the Canadian Institutes of Health Research (CIHR) Institute of Population and Public Health (IPPH) (MOP-114875; grant awarded to S.T. Leatherdale). Dr. Leatherdale is a Canadian Institutes of Health Research (CIHR)/Public Health Agency of Canada (PHAC) Chair in Applied Public Health.

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