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Canadian Journal of Public Health = Revue Canadienne de Santé Publique logoLink to Canadian Journal of Public Health = Revue Canadienne de Santé Publique
. 2020 Apr 13;111(4):515–522. doi: 10.17269/s41997-020-00313-6

Combinations of physical activity and screen time recommendations and their association with overweight/obesity in adolescents

Megan Crowe 1, Hugues Sampasa-Kanyinga 1, Travis J Saunders 2, Hayley A Hamilton 3,4, Eric I Benchimol 5, Jean-Philippe Chaput 6,✉
PMCID: PMC7438465  PMID: 32285346

Abstract

Objectives

To examine the four possible combinations of adherence to physical activity and screen time recommendations in adolescents and how the combinations relate to overweight and obesity.

Methods

A total of 9913 students in grades 7–12 were included in the present cross-sectional analyses. Moderate-to-vigorous physical activity (MVPA), screen time, and body mass index were self-reported. Multivariate logistic regression analysis was used to test the associations between combinations of MVPA (≥ 60 min/day [active] or < 60 min/day [inactive]) and screen time (≤ 2 h/day [not sedentary] or > 2 h/day [sedentary]) recommendations with overweight/obesity.

Results

We found that 53.1% of students in Ontario were considered “inactive+sedentary”, 23.7% were considered “inactive+not sedentary”, 12.1% were considered “active+sedentary”, and 11.1% were considered “active+not sedentary”. Some characteristics of “active+not sedentary” students (optimal category) included younger age, male gender, white ethnicity, higher socio-economic status, optimal sleep duration, and lower prevalence of cannabis use. After adjusting for relevant covariates, the “inactive+sedentary” group was more likely to report overweight/obesity than the “active+not sedentary” group (odds ratio [OR] = 1.71, 95% confidence interval [CI] = 1.26–2.32). The “inactive+not sedentary” group was also more likely to report overweight/obesity (OR = 1.54, 95% CI 1.20–1.97) while the “active+sedentary” group was not significantly associated with overweight/obesity (OR = 1.27, 95% CI 0.88–1.83).

Conclusion

Children meeting both the physical activity and screen time recommendations are less likely to be classified as overweight/obese compared with any other combination. Future efforts are needed to target both MVPA and sedentary behaviour to address public health concerns such as excess weight.

Keywords: Exercise, Sedentary behaviour, Excess weight, Adolescents, Public health

Introduction

The distinct constructs of physical inactivity and sedentary behaviour and their associated adverse health outcomes have become a growing area of concern in recent years (Saunders et al. 2016). Physical inactivity is defined in the scientific literature as an insufficient activity level to meet physical activity recommendations, while sedentary behaviour is any waking behaviour characterized by an energy expenditure of less than 1.5 metabolic equivalents while in a sitting, reclining or lying posture (Tremblay et al. 2017). In children and youth, physical inactivity is generally operationalized as engaging in less than 60 min of moderate-to-vigorous physical activity (MVPA) per day while high levels of sedentary behaviour are often operationalized as engaging in 2 h or more a day in recreational screen time (Tremblay et al. 2017). Recreational screen time refers to the time spent in screen behaviours that are not related to school or work (e.g., watching TV, using a smartphone/tablet, using a computer) (Tremblay et al. 2017).

Physical inactivity and sedentary behaviour are highly prevalent among adolescents (LeBlanc et al. 2017). The well-documented adverse health consequences of physical inactivity and sedentary behaviour such as excess weight among youth are of particular concern. Achieving adequate amounts of daily physical activity and limiting exposure to sedentary behaviours such as screen time are both critical in maintaining a healthy body weight (Saunders et al. 2016). However, the constructs of “physical inactivity” and “sedentary behaviour” have largely been studied in isolation from each other in previous studies (Janssen and LeBlanc 2010). One can be very active while also engaging in high amounts of sitting time throughout the day (a term coined as “active couch potato”) (Owen et al. 2010). Moreover, recreational screen time seems to have effects on body weight that are independent of physical activity. Two main mechanisms are through insufficient sleep (e.g., blue light of screens that disrupts sleep patterns and leads to weight gain) and increased food intake (especially in front of the TV through distraction) (LeBlanc et al. 2017).

Few studies have used an integrative approach to examine the combinations of physical activity and sedentary behaviour in relation to body weight and the majority have been conducted in adults or younger children (Ekelund et al. 2012; Laurson et al. 2014; Chaput et al. 2014; Hjorth et al. 2014; Loprinzi et al. 2015). Previous studies have almost entirely focused on accelerometry, which fails to get at what individuals are doing while sedentary (Saunders et al. 2016). While objective data are theoretically better than subjective data, screen time in youth is the type of sedentary behaviour that has the strongest relationship with adverse health outcomes (Saunders et al. 2016). The main observation from previous studies is that the combination of “active+not sedentary” is the optimal one for health benefits while the combination of “inactive+sedentary” is the one associated with the highest risk of adverse health outcomes (Saunders et al. 2016). However, our understanding of this issue in adolescents is incomplete and more studies are needed to quantify how combinations of physical activity and sedentary behaviour relate to body weight in this critical population characterized by very low activity levels and excessive screen behaviours (LeBlanc et al. 2017). A better understanding of this issue is also important to inform public health strategies and education campaigns.

Thus, the objective of this study was to examine the four possible combinations of adherence to the physical activity and screen time recommendations in youth and how the combinations associate with overweight and obesity. We hypothesized that the combination of “active+not sedentary” would be associated with the lowest risk of overweight/obesity while the combination of “inactive+sedentary” would be associated with the highest risk of overweight/obesity.

Methods

Study design

Data were derived from the Ontario Student Drug Use and Health Survey (OSDUHS), a repeated cross-sectional school-based survey administered every 2 years to students in grades 7 through 12 from schools across the four publicly funded school boards in Ontario, Canada: English public, French public, English Catholic, and French Catholic (Boak et al. 2018). Started in 1977, it is the longest ongoing school survey in Canada and the sample is representative of middle- and high-school students in Ontario, Canada. The survey is based on a stratified (region/school type) 2-stage (school/class) cluster design. All participants provided their signed assent in addition to parentally signed consent for those aged under 18 years. Students completed a self-administered, anonymous hard-copy questionnaire in their classroom between November 2016 and June 2017. A total of 11,435 students in grades 7–12 from 764 classes in 214 schools and 52 boards participated in the 2017 OSDUHS. The proportions for school, class, and student participation were 61%, 94%, and 61%, respectively. These numbers are above average for a survey of students that requires active parental consent (Goodman et al. 2001). Reasons for student non-response included absenteeism (11%) and unreturned consent forms or parental refusal (26%). A comparison between high-responding and low-responding classes showed no evidence of non-response bias for a set of health-related behaviours. Further methodological details are available elsewhere (Boak et al. 2018).

Measures

Dependent variable

The dependent variable in the present study was overweight/obesity. Body mass index (BMI) was based on self-reported height and weight and the questionnaire used pre-coded response options. Using the midpoint of the height and the weight response categories, BMI was calculated as weight divided by the square of height (kilograms/m2). Overweight/obesity was defined using the World Health Organization’s (2007) sex- and age-specific BMI cut-points established for children and youth (BMI z-score > +1 standard deviation). Categories included “overweight or obesity” and “healthy weight”—including for our study both the healthy and underweight categories. A sensitivity analysis excluding the underweight students (n = 506; 5% of the sample) provided similar results.

Independent variables

Physical activity

Physical activity was measured by an item that asked students how many of the last 7 days they were physically active for a total of ≥ 60 min. Students were categorized as meeting the physical activity recommendation of ≥ 60 min per day of MVPA on all 7 days, or not (Tremblay et al. 2016, 2011).

Screen time

Screen time was measured by one question that asked students how many hours a day, on average, they spent watching TV/movies; playing video/computer games; or on a computer chatting, emailing, or surfing the Internet in the last 7 days. Students meeting the screen time guideline of ≤ 2 h per day were compared with those not meeting the screen time guideline (Tremblay et al. 2016).

Combinations of physical activity and screen time

Combinations of adherence to the physical activity and screen time recommendations were defined based on meeting versus not meeting the recommendations for MVPA (≥ 60 min/day [active] or < 60 min/day [inactive]) and screen time (≤ 2 h/day [not sedentary] or > 2 h/day [sedentary]) (Tremblay et al. 2016). Students were categorized into 4 groups: “active+not sedentary” (≥ 60 min/day of MVPA and ≤ 2 h/day of screen time), “active+sedentary” (≥ 60 min/day of MVPA and > 2 h/day of screen time), “inactive+not sedentary” (< 60 min/day of MVPA and ≤ 2 h/day of screen time), and “inactive+sedentary” (< 60 min/day of MVPA and > 2 h/day of screen time).

Descriptive characteristics and covariates

Several variables were used to compare descriptive characteristics across combinations of physical activity and screen time recommendations based on availability in the dataset and their association with the dependent and independent variables.

Socio-demographic characteristics

Socio-demographic characteristics included age (in years), grade, sex (male/female), ethnicity (White, Black, East/South-East Asian, South Asian, or Other), and subjective socio-economic status (SES). Subjective SES was measured using the youth version of the MacArthur Scale of Subjective Social Status (Goodman et al. 2001). Students were asked to choose the rung that best represents their family’s place in Canadian society with respect to money, education, and occupation. Responses were treated as a scale ranging from 1 to 10; the higher the rung, the higher the perceived family subjective social status, including more money, higher education, and highly respected occupations.

Parental support

Parental support was measured by an item that asked students how often they talked about their problems or feelings with at least one of their parents. Response options ranged from “always” to “never”. For analysis purposes, responses were reverse-coded, so that higher scores indicate higher levels of parental support.

Academic performance

Academic performance was measured by the following item: “On average, what marks do you usually get in school?” Response options included “A (80–100%)”, “B (70–79%)”, “C (60–69%)”, “D (50–59%)”, and “F (below 50%)”. For analysis purposes, responses (coded 1 to 5) were reverse-coded so that higher values represent higher academic performance.

Sleep duration

Sleep duration was measured by asking students how many hours of sleep on an average school night they usually get. A dichotomous variable was constructed to represent respondents who had a sleep duration that met the recommended range (9–11 h per night for 11–13-year-olds or 8–10 h per night for 14–17-year-olds) compared with those who reported sleeping outside of this range (Tremblay et al. 2016; Hirshkowitz et al. 2015).

Eating behaviour

Students were asked how often, in the last 7 days, they consumed fruits and vegetables, sugar-sweetened beverages, and energy drinks. Response categories included “no consumption”, “1 time”, “2 to 4 times”, “5 to 6 times”, “once each day”, or “more than once each day”. A dichotomous measure was constructed to reflect adequate fruit and vegetable consumption defined as consumption of fruits or vegetables five times or more in the last 7 days versus inadequate fruit and vegetable consumption defined as consumption of fruits and vegetables less than five times in the last 7 days. Two dichotomous measures were also constructed to represent consumption (one or more times) and non-consumption of sugar-sweetened beverages and energy drinks in the last 7 days.

Substance use

Students were asked about their use of alcohol, tobacco, and cannabis using the following three questions: “In the last 12 months, how often did you drink alcohol (liquor, wine, beer, coolers)?”, “In the last 12 months, how often did you smoke cigarettes?”, and “In the last 12 months, how often did you use cannabis (e.g., “marijuana”)?” Responses were recoded to create binary measures that reflect use at least once versus non-use during the last 12 months, including a few puffs for tobacco cigarette and a sip for alcohol. Binge drinking was a binary measure of students having had 5 or more drinks of alcohol on the same occasion during the past 4 weeks, or not.

Statistical analysis

The analysis was restricted to participants with complete information on all variables included in the current study (n = 9913; 87% of the eligible participants). Individuals with missing data did not differ from those with complete information on any of the variables included in this study. We did not find significant sex interactions in the associations examined; therefore, boys and girls were pooled together for presentation. Descriptive statistics, including means, standard deviations, and frequencies (percentages), were used to summarize socio-demographic, parental, school, lifestyle, physical characteristics, eating behaviours, and substance use across combinations of physical activity and screen time recommendations. Prevalence for each of the four combinations was also computed. A Pearson’s χ2 test, adjusted for the complex survey design and transformed into an F-statistic, examined differences in categorical data and an adjusted Wald test was used for continuous data. Multivariate logistic regression analyses were conducted to identify the associations between combinations of physical activity and screen time recommendations and overweight/obesity in students. Odds ratios (OR) and 95% confidence intervals (CI) are presented for each combination of physical activity and screen time recommendations (unadjusted and fully adjusted models are presented). Covariates included in the fully adjusted models included age, sex, ethnicity, subjective SES, parental support, academic performance, sleep duration, consumption of fruits and vegetables, consumption of sugar-sweetened beverages, consumption of energy drinks, alcohol consumption, and cannabis use. All analyses were conducted with STATA 14.0 (Stata Corporation, College Station, TX, USA). Statistical significance was defined as p < 0.05 (two-tailed).

Results

The prevalence of the 4 combinations of physical activity and screen time recommendations is presented in Fig. 1. More than half of students (53.1%) were considered both inactive and sedentary. This was followed by the “inactive+not sedentary” group (23.7%), the “active+sedentary” group (12.1%), and the “active+not sedentary” group (11.1%). With regard to the distribution of data, 23% of students were physically active (60 min/day) for all 7 days, 10% for 6 days, 18% for 5 days, 12% for 4 days, and 37% for 3 days or less. For screen time, 35% of students engaged in less than 2 h per day, 36% between 3 and 4 h per day, and 29% for 5 h or more per day.

Fig. 1.

Fig. 1

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Prevalence of the four different combinations of physical activity and sedentary behaviour among adolescents in Ontario, Canada. Definition of terms: Active, ≥ 60 min/day of moderate-to-vigorous physical activity; Inactive, < 60 min/day of moderate-to-vigorous physical activity; Sedentary, > 2 h/day of screen time; Not sedentary, ≤ 2 h/day of screen time

Descriptive characteristics of the participants are reported in Table 1. Students who met both the physical activity and screen time recommendations were younger and comprised more males and students with white ethnic background. They also had a higher subjective socio-economic status and were more likely to meet sleep duration recommendations. They also had the lowest prevalence of overweight/obesity (21.7%). They were less likely to consume sugar-sweetened beverages but also less likely to consume fruits and vegetables. They were also less likely to use cannabis.

Table 1.

Comparison of descriptive characteristics across combinations of physical activity and screen time recommendations in youth

Total (n=9913) Inactive and sedentary (n=5261) Inactive and not sedentary (n=2352) Active and sedentary (n=1203) Active and not sedentary (n=1097) p value
Socio-demographics
  Age
    Mean (SD) 15.2 (1.8) 15.4 (1.7) 15.1 (1.9) 14.7 (1.8) 14.6 (1.8) <0.001
  Grade
    7 11.5 8.5 14.1 15.2 17.7 <0.001
    8 12.6 10.9 12.2 16.4 17.1
    9 16.2 14.5 16.4 20.3 19.4
    10 17.2 18.3 15.3 19.8 12.7
    11 18.4 20.4 17.3 13.5 16.5
    12 24.1 27.4 24.8 14.8 16.7
  Sex
    Females 48.9 52.3 56.5 35.1 31.4 <0.001
    Males 51.1 47.7 43.5 65.0 68.6
  Ethnic background
    White/Caucasian 56.2 53.1 56.9 58.1 67.8 <0.001
    Non-White/Other 43.8 46.9 43.1 41.9 32.2
  Subjective socio-economic status
    Mean (SD) 6.9 (1.7) 6.7 (1.6) 7.2 (1.7) 7.0 (1.8) 7.5 (1.7) <0.001
Parental characteristics
  Parental support
    Mean (SD) 2.9 (1.2) 2.9 (1.2) 3.1 (1.2) 2.8 (1.3) 3.0 (1.3) 0.006
School characteristics
  Academic performance
    Mean (SD) 4.7 (0.8) 4.6 (0.8) 4.8 (0.9) 4.6 (0.9) 4.7 (0.9) 0.007
Lifestyle
  Sleep duration recommendation
    Do not meet 66.2 71.5 61.1 63.8 53.3 <0.001
    Meet 33.8 28.5 38.9 36.3 46.7
Body mass index
  Not overweight or obese 69.4 66.3 70.7 72.3 78.3 <0.001
  Overweight or obesity 30.7 33.7 29.3 27.7 21.7
Eating behaviour
  Consumption of fruits and vegetables
    No 15.6 12.1 13.8 25.3 26.2 <0.001
    Yes 84.4 87.9 86.2 74.7 73.8
  Consumption of SSBs
    No 18.1 16.1 21.6 15.1 24.2 <0.001
    Yes 81.9 83.9 78.4 84.9 75.8
  Consumption of energy drinks
    No 86.9 88.5 86.6 82.2 84.6 0.004
    Yes 13.1 11.5 13.4 17.8 15.4
Substance use
  Tobacco cigarette smoking
    No 92.6 91.4 93.5 95.7 93.8 0.109
    Yes 7.4 8.6 6.5 4.3 6.2
  Alcohol consumption
    No 55.4 53.1 59.1 59.6 54.6 0.011
    Yes 44.6 46.9 40.9 40.4 45.5
  Cannabis use
    No 77.7 75.6 79.5 79.6 82.1 0.008
    Yes 22.4 24.4 20.5 20.4 18.0
  Binge drinking
    No 82.1 82.3 81.8 83.6 80.4 0.725
    Yes 17.9 17.7 18.2 16.4 19.6
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Data are shown as %, unless otherwise indicated

SD, standard deviation; SSBs, sugar-sweetened beverages

ap value for the comparison across the four combinations of physical activity/screen time recommendations. Pearson’s χ2 test, adjusted for survey design and transformed into an F-statistic, examined differences in categorical data and an adjusted Wald test was used for continuous data

The results of the multivariate logistic regression analysis are presented in Table 2. After adjusting for covariates, the inactive and not sedentary group was at higher odds of having overweight or obesity (OR = 1.54; 95% CI, 1.20–1.97) than those who are active and non-sedentary. The inactive and sedentary group was also more likely to have overweight or obesity than the active and not sedentary group (OR = 1.71; 95% CI, 1.26–2.32). The odds for overweight/obesity did not reach statistical significance for the active and sedentary group (OR = 1.27; 95% CI, 0.88–1.83).

Table 2.

Associations between combinations of physical activity and screen time recommendations with excess weight

Overweight or obesity (n = 9913)
OR (95% CI) p value
Model 1
     Active and not sedentary Reference
     Active and sedentary 1.38 (0.95–1.99) 0.088
     Inactive and not sedentary 1.49 (1.17–1.91) 0.002
     Inactive and sedentary 1.83 (1.38–2.43) < 0.001
Model 2
     Active and not sedentary Reference
     Active and sedentary 1.27 (0.88–1.83) 0.202
     Inactive and not sedentary 1.54 (1.20–1.97) 0.001
     Inactive and sedentary 1.71 (1.26–2.32) 0.001
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Model 1, unadjusted; Model 2, adjusted for age, sex, ethnicity, subjective socio-economic status, parental support, academic performance, sleep duration, consumption of fruits and vegetables, consumption of sugar-sweetened beverages, consumption of energy drinks, alcohol consumption, and cannabis use

OR, odds ratio; CI, confidence interval

Discussion

The aim of this study was to examine adherence to the four possible combinations of physical activity and screen time recommendations in adolescents and how they relate to overweight and obesity. We observed that over half of adolescents in this sample do not meet the physical activity and screen time recommendations while only 11% meet both. Characteristics associated with meeting both recommendations included younger age, male gender, white ethnicity, higher socio-economic status, meeting sleep duration recommendations, lower consumption of sugar-sweetened beverages and fruits and vegetables, and lower prevalence of cannabis use. The findings also indicate that meeting both recommendations is the optimal combination as it relates to lower odds of overweight/obesity. Meeting the screen time recommendation does not offset the risk of overweight/obesity when paired with not meeting the physical activity recommendation (i.e., the “inactive/not sedentary” group had a higher odds ratio). Conversely, meeting the physical activity recommendation but not meeting the screen time recommendation confers no greater risk than meeting both the physical activity and screen time recommendations (i.e., similar odds ratio in the “active/sedentary” group and the “active/not sedentary” group). This suggests that higher physical activity levels may be more critical than lower screen time levels for a lower prevalence of overweight/obesity in youth.

Previous studies have examined relationships between the adherence to independent recommendations of physical activity and screen time as they relate to body composition (Prentice-Dunn and Prentice-Dunn 2012; Chaput et al. 2012). However, very few have looked at the adherence to combinations of physical activity and screen time recommendations and the association with having overweight/obesity among a teenage population, a critical developmental time period where lifestyle behaviours can have long-term implications (Carlson et al. 2010; Ekelund et al. 2012; De Bourdeaudhuij et al. 2013; Laurson et al. 2014; Chaput et al. 2014; Hjorth et al. 2014; Herman et al. 2014; 2015; Katzmarzyk et al. 2015; Loprinzi et al. 2015; Aggio et al. 2015). Most previous studies also used accelerometry so the type of sedentary behaviour was not considered (Saunders et al. 2016).

Few previous studies have specifically addressed combinations of physical activity and sedentary behaviour with adiposity in adolescents (Katzmarzyk et al. 2015). Those that explored these combinations found that the ideal combination of high levels of physical activity and low levels of sedentary behaviour was associated with lower adiposity (Saunders et al. 2016). They also indicated a lower prevalence of overweight/obesity among the highly active/not sedentary group compared with in the inactive/sedentary group (Saunders et al. 2016). Additionally, higher levels of physical activity were associated with favourable levels of adiposity irrespective of sedentary behaviours in an international sample of children, suggesting that physical activity may be more critical than sedentary behaviour in predicting adiposity outcomes (Katzmarzyk et al. 2015). This is in line with the results of the present study. Future research will need to develop these categorical groups into continuous variables to look at dose-response of levels of physical activity and sedentary behaviour.

In addition to adding to the body of literature addressing the association between specific combinations of physical activity and sedentary behaviour, the present study also looked at characteristics of youth across the four combinations of physical activity and sedentary behaviour. Characteristics including socio-demographics, substance use, and eating behaviours were assessed. Most results were as expected with the exception of consumption of fruits and vegetables. Contrary to expectation, the group who was highly active and not sedentary reported consuming the fewest fruits and vegetables, with 73.8% of the sample reporting an adequate intake as opposed to 87.9% in the inactive and sedentary group. This surprising finding may be a result of the self-report nature of the questionnaire and attributable to respondent error that is typically seen with dietary data. It is possible that the ideal group is underreporting their weekly consumption or that the sedentary group is overreporting. It is also possible that some groups have less access to fresh fruits and vegetables despite meeting the guidelines for physical activity and screen time.

The results of the present study have implications for informing public health strategies and educational policy. Policy-makers, educators, and caregivers require awareness of the relationship between physical activity and sedentary behaviour guideline adherence and body composition in youth. This information could be utilized to: (i) inform government policy with respect to integration of physical activity into schools beyond the elementary level; (ii) provide a scientific basis for school mandated programming which contribute to ensuring that youth meet the recommended 60 min of MVPA and minimize their sedentary time during school hours; and (iii) educate and encourage parents to move towards limit setting regarding daily screen time usage. For example, the importance of limit setting could be stressed to caregivers to maintain rules limiting screen time during teenage years via public service messaging (Carlson et al. 2010). Education ministries and school boards could also implement programs such as Ontario’s Daily Activity into secondary schools, which ensure that students have the opportunity to be physically active during the day, requiring a minimum of 20 min of MVPA to be incorporated into each school day.

The strengths of this study stem from the large representative sample of Ontario middle- and high-school students. However, the limitations of the present study must be considered. The findings may be the product of the phrasing of the questions in the OSDUHS. For example, the screen time variable presents a question which asked students how many hours a day, on average, they spent watching TV/movies; playing video/computer games; or on a computer chatting, emailing, or surfing the Internet in the last 7 days. The self-reported nature of the survey may result in over- or underestimates or cause them to neglect other forms of screen time not defined in the question. Similarly, physical activity was measured by an item that asked students how many of the last 7 days they were physically active for a total of ≥ 60 min each day. The level of activity is not explicitly defined which may have resulted in students omitting activities based on their subjective assessment of what they deemed as “active”. The responses provided by students to the survey questions may have been subject to recall bias, respondent burden, or disclosure error. BMI is also limited in its ability to capture adiposity and is a crude indicator of health. Furthermore, causality cannot be established from the study results due to the cross-sectional design. Therefore, it is not possible to make a determination of the direction of the associations between physical activity, sedentary behaviour, and excess weight. Future longitudinal studies will help to establish the existence of a temporal relationship and to assess causality. Additionally, the results cannot be generalized to younger or older age groups outside of the measured age range. Finally, although the present study did not find sex interactions in the associations examined, future studies in this area will need to assess maturation and sex-specific effects of physical activity.

Despite the limitations, this study adds to the growing body of literature assessing the association between combinations of physical activity and sedentary behaviour and excess weight in an adolescent population. This study found that not meeting the physical activity and screen time recommendations is highly prevalent in Ontario students. Younger age, male gender, white ethnicity, higher socio-economic status, meeting sleep duration recommendations, lower consumption of sugar-sweetened beverages and fruits and vegetables, and lower prevalence of cannabis use were characteristics associated with meeting both the physical activity and screen time recommendations. Adolescents more likely to have overweight/obesity were those in the “inactive/sedentary” group, followed by the “inactive/not sedentary” and the “active/sedentary” group in comparison with those who meet both the physical activity and screen time recommendations. Future research should use objective measures and a longitudinal study design to address the limitations of the present study.

Acknowledgements

The Ontario Student Drug Use and Health Survey is an initiative of the Centre for Addiction and Mental Health that is funded by the ongoing support of the Ontario Ministry of Health and Long-Term Care, along with targeted funding from various provincial agencies.

Compliance with ethical standards

Ethics approval was granted by the Research Ethics Boards at the Centre for Addiction and Mental Health, the 31 school board research review committees, and York University.

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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