(2024) 24:596
de Victo et al. BMC Pediatrics
https://doi.org/10.1186/s12887-024-05079-1

BMC Pediatrics

RESEARCH

Open Access

Association of physical activity and sitting
time with tobacco and alcohol use in 222,495
adolescents from 66 countries
Eduardo Rossato de Victo1*, Gerson Ferrari2,3, Clemens Drenowatz4 and Dirceu Solé1

Abstract
Background The prevalence of tobacco and alcohol use among adolescents is alarming, and these substances
are among the leading risk factors for current and future health among adolescents. Physical activity has the potential to help prevent substance use among adolescents. The objective of this study was to evaluate the association
between physical activity, modes of transportation to or from school, and sitting time with tobacco and alcohol use
among 222,495 adolescents.
Methods This cross-sectional study used data from national surveys conducted in 66 countries, obtained
through the Global School-based Student Health Survey, and included adolescents aged 11 to 17 years. Information on physical activity, transportation to or from school, sitting time, and tobacco and alcohol use was collected through self-report questionnaires. Generalized linear models were employed to estimate the associations
between these variables.
Results The analysis, adjusted for sex, age, and region, revealed that being physically active was associatedwith lower odds of smoking (OR: 0.86, 95%CI: 0.83–0.89), alcohol use (OR: 0.74, 95%CI: 0.72–0.76), binge drinking (OR:0.66, 95%CI: 0.62–0.69), and drunkenness (OR: 0.85, 95%CI: 0.83–0.88) compared to inactivity. Insufficiently active
participants also had lower odds of tobacco use (OR: 0.83, 95%CI: 0.80–0.85), alcohol use (OR: 0.77, 95%CI: 0.75–0.79),
binge drinking (OR: 0.91, 95%CI: 0.87–0.96), and drunkenness (OR: 0.88, 95%CI: 0.85–0.90) compared to inactive participants. Additionally, active transportation to or from school was associated with lower odds of tobacco use (OR: 0.97,
95%CI: 0.95–0.99), alcohol use (OR: 0.94, 95%CI: 0.92–0.96), and binge drinking (OR: 0.78, 95%CI: 0.75–0.81) comparedto those using passive transportation. Participants with acceptable sitting time, however, were more likely to use
tobacco (OR: 1.48, 95%CI: 1.45–1.52), use alcohol (OR: 1.68, 95%CI: 1.64–1.72), binge drink (OR: 1.68, 95%CI: 1.62–1.75),
and experience drunkenness (OR: 1.66, 95%CI: 1.62–1.69) compared to those with excessive sitting time.
Conclusion Being physically active, even at insufficient levels, may have beneficial effects on tobacco and alcohol
use in adolescents. Acceptable sedentary time, on the other hand, was positively associated with tobacco and alcoholuse.
Keywords Physical exercise, Use of tobacco, Alcohol drinking, Binge drinking, Adolescent, Global School Based
Student Health Survey, Global Youth Tobacco Survey*Correspondence:
Eduardo Rossato de Victoeduardo.rossato@unifesp.br
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
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de Victo et al. BMC Pediatrics(2024) 24:596

Introduction
According to the World Health Organization (WHO),
adolescence is defined as the second decade of life, ranging from 10 to 19 years of age. This period is characterized by a complex process of growth and biopsychosocial
development, involving anatomical, physiological, psychological, and social changes [1]. Due to the sensitivity of this stage, adolescents are at an increased risk forhealth problems that are related to the experimentation
and use of psychoactive substances such as tobacco, alcohol, and other drugs [2–4].
The prevalence of tobacco and alcohol use among adolescents is alarming. Data from the Global School-based
Student Health Survey (GSHS) indicate that 13.6% of
adolescents reported using tobacco, and 1 in 4 drank
alcohol in the month prior to the survey [5, 6]. According
to the Global Burden of Diseases, Injuries, and Risk Factors data, tobacco and alcohol use rank among the leading risk factors for global health [7]. The public healthimpact of these substances could be reduced with greater
efforts to prevent their use during adolescence, as use
typically begins in this period and is linked to various
chronic non-communicable diseases in adulthood [8].
Accordingly there is growing concern of public authorities and experts on the subject who are increasingly
warning about the dangers of using these substances,
especially during adolescence [9, 10].
One strategy to prevent the use of substances like
tobacco and alcohol is promoting physical activity (PA).
PA is defined as any voluntary movement produced bymuscles that results in energy expenditure above resting levels [11]. The WHO recommends that children
and adolescents engage in at least 60 min of moderate
to vigorous PA per day due to its numerous health benefits. The organization also emphasizes that any amountof PA is better than none [12]. PA can be accumulated
across various domains, such as playing sports during leisure time or through active transportation to and fromschool [12]. Specifically, PA can help counteract the useof tobacco, alcohol, and other drugs through psychological mechanisms by reducing cravings and impulses,
as well as by controlling behaviors that may lead to substance use [13, 14]. Additionally, involvement in PA may
be limiting the exposure to these substances and increaseinteractions with individuals who are more interested inmaintaining healthy habits [13–15].
Physical inactivity, which is characterized by the complete absence of moderate to vigorous PA, on the other
hand, poses a serious threat to public health as it contributes to numerous health problems [16]. Accordingly,
extensive periods of sedentary behavior, which refers
to any waking activity with low energy expenditure,such as sitting [17], has been shown to be a risk marker

Page 2 of 9

for alcohol use in adults but there remains a need forresearch exploring these associations in adolescents [17].
Despite the increasing volume of data on this topic, thequality of evidence remains limited, and the literature
lacks sufficient data to either support or refute the preventive role of PA in combating tobacco and alcohol use[13–15]. There is a pressing need for studies with larger
sample sizes that include children and adolescents, particularly those conducted in countries outside the major
global economies [13–15].
To address this gap, the present study aimed to evaluate the association between PA (including leisure sports
and exercising with friends), transportation to or fromschool, and sitting time (excluding time spent sitting at
school or doing homework) with tobacco and alcohol use
among adolescents from different regions of the world.

Methods
Study design and sample

The study was conducted using national survey dataobtained from the GSHS. Full details of the GSHS designand sampling methods are reported elsewhere [18]. The
GSHS is a collaborative surveillance project designed tohelp countries measure and assess behavioral risk factors
and protective factors across 10 key areas among adolescents between 11 and 17 years of age. This relatively
low-cost, school-based survey uses a self-administeredquestionnaire to gather data on youth health behaviors and protective factors related to the leading causesof morbidity and mortality among children and adults
worldwide [19, 20]. Questionnaires are completed during regular school hours using a computer-scanned,
self-report form, and are translated into the corresponding language of each country. To obtain a nationally representative sample of adolescents, the study employeda two-stage sampling method. First, schools were randomly selected based on probability proportional tosize. Then, within those selected schools, classes wererandomly chosen to participate, with all students in theselected classes invited to participate [20].
The GSHS began in 2003 and continues to this day.
Countries participated in different years, so the data
available varies by country and survey edition. It is recommended that countries participate periodically, which
is why some countries have participated in the survey more than once. The data collection period for thisstudy included the years available on the GSHS website
up until June 2020, encompassing 102 countries. For fivecountries (Zimbabwe, Chile, China, Ecuador, and Occupied Palestinian Territory), only regional or city data
were available; all other estimates were nationally representative [21]. For countries that participated more thanonce, only data from the most recent collection period

de Victo et al. BMC Pediatrics(2024) 24:596

were considered [22]. This resulted in an initial sample
size of 388,381 adolescents. However, some countries
did not have data on one or more of the variables considered in this study, and missing data on one or morevariables of interest were used as exclusion criteria. Afterexcluding participants with missing data, the final sample
for this study consisted of 222,495 participants from 66
countries.
Ethical considerations

The GSHS adhered to human subjects’ protection standards. Each participating country approved the research
project through their local government administrations.
Additionally, all studies received approval from ethics
committees in their respective countries and followed thestandards established by the WHO. Data from this study
and detailed methods used in the GSHS are described
online on the WHO website (https://​www.​who.​int/​
teams/​nonco​mmuni​cable-​disea​ses/​surve​illan​ce/​syste​ms-​
tools/​global-​school-​based-​stude​nt-​health-​survey/​metho​
dology) and in other publications [20, 22, 23]. Participation was voluntary; adolescents provided their assent,
and informed consent was obtained from their parents
or guardians [20, 21]. All methods were performed inaccordance with the Helsinki Declaration and guidelines.
Physical activity, transportation to or from schooland sitting time

Total PA was assessed using the following question: “During the last 7 days, on how many days did you engage
in PA for a total of at least 60 minutes per day?” (Addup all the time you spent in any kind of PA each day).
Responses ranged from 0 to 7 days. PA was defined as
any activity that increases the heart rate or leaves the student out of breath. This includes leisure sports, exercisingwith friends, or active transportation to or from school[24]. Based on their responses, adolescents were classified as physically inactive (0 days), insufficiently active (1to 4 days), or physically active (5 or more days) [25].
Transportation to or from school refers to how adolescents travel between home and school. This was assessedwith the question, “In the last 7 days, on how many days
did you walk or ride a bicycle to or from school?” Possible answers ranged from 0 to 7 days. Adolescents whowalked or cycled to or from school at least 3 days a week
were considered to be engaging in active transportation,
as 3 days represent the majority of school days in a week
[26].
Time spent sitting was evaluated with the question,“How much time do you spend on a typical day sitting
and watching television, playing on the computer, talking
with friends, or doing other activities while seated, suchas surfing the internet?” (Excluding time spent sitting at

Page 3 of 9

school or for homework) [24]. Response options included“less than 1 hour/day,” “1 to 2 hours/day,” “3 to 4 hours/
day,” “5 to 6 hours/day,” “7 to 8 hours/day,” or “more than
8 hours/day.” A cut-off of 3 or more hours/day was used
to define excessive sedentary time, based on previous
associations with adverse health outcomes [27].
Tobacco and alcohol use

Tobacco use was assessed with the question, “In the last
30 days, on how many days did you smoke?” The response
options were: “0 days,” “1 to 2 days,” “3 to 5 days,” “6 to9 days,” “10 to 19 days,” “20 to 29 days,” or “every day.”
Alcohol use was measured by asking, “During the last 30
days, on how many days did you have at least one drink
containing alcohol?” with the response options being “0days,” “1 to 2 days,” “3 to 5 days,” “6 to 9 days,” “10 to 19
days,” “20 to 29 days,” or “every day.” Both variables werecategorized as not used (0 days) or used (1 or more days)[28].
Binge drinking was identified based on the frequencyof drinks used in the last 30 days, using the question, “On
the days when you drank alcohol in the last 30 days, howmany drinks did you consume per day?” Possible answers
were: “Didn’t drink in the last 30 days”, “less than 1 drink”,“1 drink”, “2 drinks”, “3 drinks”, “4 drinks”, “5 or more
drinks”. According to the questionnaire, drinking alcohol
does not include taking a few sips of wine for religious
purposes. A ‘drink’ is defined as a glass of wine, a bottleof beer, a small glass of liquor, or a mixed drink. Those
who consumed 5 or more drinks on the same day wereclassified as binge drinkers [29].
The number of times participants had been drunk wasassessed with the question, “How many times have you
been drunk in your life?” Response options were: “None,”“1 to 2 times,” “3 to 9 times,” or “10 or more times.” Thisvariable was categorized as no (never) or yes (one ormore times) [30].
Demographic data

Data on age and sex were collected through the questionnaire. Information about the countries and geographic regions of the participants was extracted fromthe respective country databases available on the WHO
website(https://​www.​who.​int/​teams/​nonco​mmuni​
cable-​disea​ses/​surve​illan​ce/​syste​ms-​tools/​global-​school-​based-​stude​nt-​health-​survey/​metho​dology) [20].
Statistical analysis

Descriptive statistics are presented as frequencies andpercentages for tobacco use, alcohol use, binge drinking,and drunkenness.
Generalized linear models with a binomial link function were used to estimate the associations between

de Victo et al. BMC Pediatrics(2024) 24:596

independent (i.e., PA, transportation to or from schooland sitting time) and dependent variables (i.e., tobaccouse, alcohol use, binge drinking, and drunkenness) due
to their dichotomous nature. Binomial logistic regression
analyses were conducted, and odds ratios (OR) with 95%
confidence intervals (95% CI) were reported. Associations were adjusted using the following models: (i) model
1, mutually adjusted for independent variables with thelevel of PA categorized into three categories (i.e., inactive
vs. insufficiently active vs. active); (ii) model 2, included
model 1 plus sex, age, and region.
The Wald test and the likelihood ratio test were primarily used to assess the significance of the models’ betacoefficients. Multicollinearity among the independent
variables was examined using Variance Inflation Factor (VIF) values (acceptable: <10) and tolerance levels(acceptable: >0.25). All analyses incorporated the expansion technique and sample weights to ensure adherence
to the selection process and population representativeness as outlined by the GSHS. The significance level was
set at 5%. Analyses were conducted using R software, version 4.2.3.

Results
The sample consisted of 222,495 adolescents, 53.6% ofwhom were female. Among the participants, 26.0% were
inactive, 58.6% used passive transportation to or fromschool and 37.6% had excessive sitting time (> 3 h/day).
Additionally, 12.6% used tobacco and 24.8% used alcohol
at least once in the 30 days prior to data collection. Binge
drinking was reported by 4.9% of the sample, and 18.7%
had experienced at least one episode of drunkenness intheir lives (Table 1). Results by country are detailed in the
supplementary material (Tables S1 and S2).
Table 2 presents the results of the logistic regression
analysis, adjusted for sex, age, and geographic region.
Being active was associated with a lower likelihood of
tobacco use (OR: 0.86, 95%CI: 0.83–0.89), alcohol use(OR: 0.74, 95%CI: 0.72–0.76), binge drinking (OR: 0.66,
95%CI: 0.62–0.69), and drunkenness (OR: 0.85, 95%CI:
0.83–0.88). Participants with insufficient PA were alsoless likely to use tobacco (OR: 0.83, 95%CI: 0.80–0.85),
use alcohol (OR: 0.77, 95%CI: 0.75–0.79), report binge
drinking (OR: 0.91, 95%CI: 0.87–0.96), and experience
drunkenness (OR: 0.88, 95%CI: 0.85–0.90) compared toinactive participants. Those who used active transportation were less likely to use tobacco (OR: 0.97, 95%CI:
0.95–0.99), use alcohol (OR: 0.94, 95%CI: 0.92–0.96), andreport binge drinking (OR: 0.78, 95%CI: 0.75–0.81), compared to those using passive transportation. Conversely,
the group with acceptable sitting time was more likely
to use tobacco, use alcohol, and report binge drinking
and drunkenness compared to the group with excessive

Page 4 of 9

sitting time (­ORtobacco: 1.48, 95%CI: 1.45–1.52; O­ Ralcohol:
1.68, 95%CI: 1.64–1.72; ­ORbingedrinking: 1.68, 95%CI: 1.62–
1.75; ­ORdrunkenness: 1.66, 95%CI: 1.62–1.69).
Table 3 presents the results of multiple logistic regression analyses, examining the independent variables
simultaneously in two models. Model 1 indicates that
insufficiently active participants had lower odds oftobacco use and smoking compared to inactive participants ­(ORtobacco: 0.79, 95% CI: 0.76–0.81; O­ Ralcohol: 0.74,
95%CI: 0.72–0.76; O­ Rbingedrinking: 0.86, 95%CI: 0.81–0.91;­ORdrunkenness: 0.84, 95%CI: 0.82–0.86). These results
remained essentially unchanged after adjusting for sex,age, and region (Model 2), except for binge drinking,which was no longer significant. Physically active participants were less likely to use tobacco or alcohol compared to inactive participants, even after adjustment forsex, age, and region (­ORtobacco: 0.88, 95%CI: 0.85–0.92;­ORalcohol: 0.77, 95%CI: 0.74–0.79; O­ Rbingedrinking: 0.70,
95%CI: 0.67–0.75; O­ Rdrunkenness: 0.88, 95%CI: 0.85–0.90).
Compared to those using passive transportation, individuals using active transportation were less likely to
use alcohol and engage in binge drinking; however, after
adjustment, only binge drinking remained significant(OR: 0.82, CI95%: 0.79–0.86). Those with acceptable
sitting time were more likely to exhibit all of the aforementioned outcomes, even after adjustments (­ORtobacco:
1.47, 95%CI: 1.43–1.51; ­ORalcohol: 1.66, 95%CI: 1.63–1.70;­ORbingedrinking: 1.67, 95%CI: 1.60–1.74; ­ORdrunkenness: 1.65,
95%CI: 1.61–0.69).

Discussion
This study investigated the association between PA,
active transportation, and sedentary time with tobaccoand alcohol use among adolescents. The results indicatedthat being active, even below PA recommendations, wasbeneficially associated with substance use, independentof sex, age, and region. This underscores the principlethat “some PA is better than none” and that “additional
benefits occur with more PA“ [31, 32]. Thus, accumulating PA, even if it falls short of recommended levels, canserve as an important correlate of reduced tobacco andalcohol use.
A systematic review of longitudinal studies thatexamined the relationship between participation in
sports and the use of alcohol and illicit drugs duringlate adolescence, on the other hand, showed a positive
association between alcohol consumption and sports
participation [33]. It was, however, noted that sports
participation acted as a protective factor against illicit
drug use. Similarly, cross-sectional studies have showna negative association between sports and drug use
while results on the association between sports participation and alcohol consumption have been equivocal

de Victo et al. BMC Pediatrics(2024) 24:596

Page 5 of 9

Table 1 Description [n(%)] of the demographic, lifestyle and behavioral characteristics of the adolescents
Total
222,495

Tobacco use

Alcohol use

Binge drinking

Drunkenness

Yes
27,946(12.6)

No
194,549(87.4)

Yes
55,072(24.8)

No
167,423(75.2)

YesNo
10,797 (4.9) 211,698(95.1)

Yes
41,608(18.7)

No
180,887(81.3)

Sex
Male

103,185(46.4)

17,454 (16.9)

85,731 (83.1)

28,048 (27.2)

75,137 (72.8)

5628 (5.5)

97,557 (94.5)

22,833 (22.1)

80,352 (77.9)

Female119,310(53.6)

10,492 (8.8)

108,818(91.2)

27,024 (22.7)

92,286 (77.3)

5169 (4.3)

114,141(95.7)

18,775 (15.7)

100,535 (84.3)

Age (years)
112390 (1.1)

210 (8.8)2180 (91.2)

338 (14.1)

2052 (85.9)

37 (1.5)2353 (98.5)

233 (9.7)2157 (90.3)

12

13,598 (6.1)

743 (5.5)

12,855 (94.5)

1674 (12.3)

11,924 (87.7)

143 (1.1)

13,455 (98.9)1117 (8.2)

12,481 (91.8)

13

39,786 (17.9)3080 (7.7)

36,706 (92.3)

6843 (17.2)

32,943 (82.8)

927 (2.3)

38,859 (97.7)

4225 (10.6)

35,561 (89.4)

1450,483 (22.7)

5574 (11.0)

44,909 (89.0)

11,857 (23.5)

38,626 (76.5)

2130 (4.2)

48,353 (95.8)

7850 (15.5)

42,633 (84.5)

1549,485 (22.2)

6810 (13.8)

42,675 (86.2)

13,856 (28.0)

35,629 (72.0)

3021 (6.1)

46,464 (93.9)

10,282 (20.8)

39,203 (79.2)≥ 16

66,753 (30.0)

11,529 (17.3)

55,224 (82.7)

20,504 (30.7)

46,249 (69.3)

4539 (6.8)

62,214 (93.2)

17,901 (26.8)

48,852 (73.2)

RegionAfrica33,709 (15.2)2604 (7.7)

31,105 (92.3)

6804 (20.2)

26,905 (79.8)

569 (1.7)

33,140 (98.3)

5889 (17.5)

27,820 (82.5)

Europe1649 (0.7)

266 (16.1)

1383 (83.9)

712 (43.2)

937 (56.8)

55 (3.3)

1594 (96.7)

459 (27.8)

1190 (72.2)

America

73,046 (32.8)

11,656 (16.0)

61,390 (84.0)

31,477 (43.1)

41,569 (56.9)

7595 (10.4)

65,451 (89.6)

20,720 (28.4)

52,326 (71.6)

Eastern
Mediterranean

4819 (2.2)

566 (11.7)

4253 (88.3)

647 (13.4)

4172 (86.6)

69 (1.4)4750 (98.6)

501 (10.4)

4318 (89.6)

Westernpacific

75,355 (33.9)8580 (11.4)

66,775 (88.6)

11,524 (15.3)

63,831 (84.7)

2143 (2.8)

73,212 (97.2)

10,259 (13.6)

65,096 (86.4)

Southeast
Asia

33,917 (15.2)

4274 (12.6)

29,643 (87.4)

3908 (11.5)

30,009 (88.5)

366 (1.1)

33,551 (98.9)3780 (11.1)

30,137 (88.9)5974 (10.3)

52,037 (89.7)

11,488 (19.8)

46,523 (80.2)

2133 (3.7)

55,878 (96.3)

9242 (15.9)

48,769 (84.1)

Insuffi110,032
ciently active (49.5)(1–4 days/
week)

14,455 (13.1)

95,577 (86.9)

28,555 (26.0)

81,477 (74.0)

5092 (4.6)

104,940(95.4)

21,105 (19.2)

88,927 (80.8)

Active (≥ 5days/week)

7517 (13.8)

46,935 (86.2)

15,029 (27.6)

39,423 (72.4)

3572 (6.6)

50,880 (93.4)

11,261 (20.7)

43,191 (79.3)

Physical activity
Inactive (0days/week)

58,011 (26.0)

54,452 (24.5)

Transportation to or from school
Passive

130,337(58.6)

15,947 (12.2)

114,390(87.8)

30,960 (23.8)

99,377 (76.2)

5419 (4.2)

124,918(95.8)

23,740 (18.2)

106,597 (81.8)

Active92,158 (41.4)

11,999 (13.0)

80,159 (87.0)

24,112 (26.2)

68,046 (73.8)

5378 (5.8)

86,780 (94.2)

17,868 (19.4)

74,290 (80.6)

Excess(≥ 3 h/day)

83,731 (37.6)

13,310 (15.9)

70,421 (84.1)

27,727 (33.1)

56,004 (66.9)

6087 (7.3)

77,644 (92.7)

20,943 (25.0)

62,788 (75.0)

Acceptable (< 3 h/day)138,764(62.4)

14,636 (10.5)

124,128(89.5)

27,345 (19.7)

111,419(80.3)

4710 (3.4)

134,054(96.6)

20,665 (14.9)

118,099 (85.1)

Sitting time[33]. Another study that explored the relationshipbetween PA, physical education classes, and sports participation with substance use among adolescents also
identified PA as an important protective factor against
tobacco and drug use, while it was not found to be protective against alcohol use [34].

A notable aspect of the literature on this topic, however, is the frequent use of the term “sports participation”
rather than “PA” when investigating the relationship with
substance use. Although these terms have some similarities, they are distinctly different. “Sports participation”
is more closely associated with organized sports and is

de Victo et al. BMC Pediatrics(2024) 24:596

Page 6 of 9

Table 2 Multivariate logistic regression estimating the probability of association of each independent variable
Tobacco use

Alcohol use

Binge drinking

Drunkenness

OR (95% CI)

OR (95% CI)

OR (95% CI)

OR (95% CI)

Physical activity
11

Insufficiently active (1–4 days/week) 0.83 (0.80–0.85)

Inactive (0 days/week)

1

0.77 (0.75–0.79)

0.91 (0.87–0.96)

0.88 (0.85–0.90)

Active (≥ 5 days/week)

0.74 (0.72–0.76)

0.66 (0.62–0.69)

0.85 (0.83–0.88)

0.86 (0.83–0.89)

Transportation to or from school
Passive

1

1

1

1

Active0.97 (0.95–0.99)

0.94 (0.92–0.96)

0.78 (0.75–0.81)

0.99 (0.97–1.02)

Sitting time
Excess (≥ 3 h/day)

1

1

1

1

Acceptable (< 3 h/day)

1.48 (1.45–1.52)

1.68 (1.64–1.72)

1.68 (1.62–1.75)

1.66 (1.62–1.69)

Presented values: odds ratio (OR) with the respective 95% confidence interval (95% CI)
Analysis adjusted for sex, age and region

Table 3 Multiple logistic regression adjusted for independent and adjustment variables
Tobacco use

Alcohol use

Binge drinking

Drunkenness

OR (95% CI)

OR (95% CI)

OR (95% CI)

OR (95% CI)

Model 1
Insufficiently active (1–4/days)

0.79 (0.76–0.81)

0.74 (0.72–0.76)

0.86 (0.81–0.91)

0.84 (0.82–0.86)

Active (≥ 5/days)

0.75 (0.72–0.78)

0.69 (0.67–0.71)

0.62 (0.59–0.65)

0.77 (0.74–0.79)

Transportation active

0.98 (0.95–1.01)

0.94 (0.92–0.96)

0.77 (0.74–0.80)

0.98 (0.96–1.00)

Sitting time acceptable (< 3 h/day)

1.58 (1.54–1.62)

1.98 (1.94–2.02)

2.18 (2.09–1.26)

1.88(1.84–1.92)

Model 2Insufficiently active(1–4 days/week)

0.85 (0.82–0.88)

0.79 (0.77–0.81)

0.96 (0.91–1.01)

0.90 (0.88–0.93)

Active (≥ 5 days/week)

0.88 (0.85–0.92)

0.77 (0.74–0.79)

0.70 (0.67–0.75)

0.88 (0.85–0.90)

Transportation active

0.99 (0.97–1.02)

0.99 (0.96–1.01)

0.82 (0.79–0.86)

1.02 (1.00–1.05)

Sitting time acceptable (< 3 h/day)

1.47 (1.43–1.51)

1.66 (1.63–1.70)

1.67 (1.60–1.74)

1.65 (1.61–1.69)

Presented values: odds ratio (OR) with the respective 95% confidence interval (95%CI); independent variables: level of physical activity, transportation to or fromschool and sitting time
Model 1 mutually adjusted for independent variables with the level of physical activity categorized into three categories
Model 2 included model 1 plus sex, age, and region

influenced by cultural, social, and psychological factorsthat can promote alcohol use. This association is further reinforced by commercial interests that use sports
to market al.coholic beverages and often link sports withalcohol in media representations [35]. In contrast, ourstudy focused solely on PA and found it to be associatedwith lower use of tobacco and alcohol. El Kazdouh et al.
also showed that individuals who are motivated to maintain their health are more likely to avoid substance use byengaging in beneficial activities such as PA. This supports
the notion that PA can be a valuable tool for preventing
substance use [36].
Another review that focused on PA, rather than sports
participation, included only studies using questionnaire
data, similar to the present study. This review included

research from the USA, Canada, Europe, Brazil, andSouth Africa and found a positive association between
PA and alcohol use in 14 studies, a negative associationin 6 studies, and no association in 17 studies [17]. Thesefindings suggest that the impact of PA and sports participation on tobacco and alcohol use can vary, dependingon the sample and geographic location.
Active transportation to or from school is anotherimportant lifestyle behavior that has been linked tohealth, healthy habits, and the accumulation of PA [37].
For this study, active transportation was defined as walking or cycling to or from school on at least 3 days a week,
representing more than half of the school week. This classification aligns with other studies using the same database and similar questionnaires [26]. The study foundde Victo et al. BMC Pediatrics(2024) 24:596

that active transportation was also associated with alower likelihood of tobacco and alcohol use, binge drinking, and a history of drunkenness. After adjusting forsex, age, and region, these associations remained largely
unchanged, except for drunkenness, where the significance was lost. These findings underscore the importanceof active transportation for promoting health among
adolescents. It is associated not only with increased PA
and reduced sedentary behavior but also with lower rates
of tobacco and alcohol use, as well as binge drinking.
The role of sitting time in the epidemiology of inactivity has been increasingly debated, but results remaininconclusive [38, 39]. Our study hypothesized that sitting
for an acceptable amount of time (less than 3 h) would
be associated with lower tobacco and alcohol use. Contrary to this expectation, the data revealed a significantly
higher risk of tobacco and alcohol use in the group withacceptable sitting time compared to those with excessive sitting time. These results are also in contrast with asystematic review that showed a predominance of studies indicating a negative association between sedentary
behavior and alcohol use [17]. Differences in sitting timecategorization among studies that found a negative association, such as using 4 h as a cutoff or employing scores,
weighted averages, or quartiles and quintiles [40–43]
may explain these conflicting results. Our categorization
might have contributed to these discrepancies. Although
we lack a clear recommendation for optimal sitting time,
our findings suggest that the 3-hour cutoff [39, 44, 45]
could be linked to other health risks but not necessarily
to tobacco and alcohol use [44, 45].
However, a previous study has also observed a higher
risk of smoking among those who spend less time sitting [42]. One hypothesis for the lack of sedentary time
being a protective factor against tobacco use is that muchof this sitting time occurs in front of a TV or computer,activities generally performed indoors where tobacco use
is often restricted by family rules [42]. Additionally, many
countries have smoking restrictions in places frequented
by young people, such as cinemas and restaurants.
Our findings, combined with the discussion on theassociation between PA, active transportation, andsedentary behavior with tobacco and alcohol use, highlight the complexity of these behaviors. Several risk andprotective factors influence substance use, including
the availability of substances, family history, individual
characteristics like sensation seeking, and social factors such as substance use among peers [46–48]. These
factors operate at various levels community (e.g., local
availability), family (e.g., parental history), individual(e.g., sensation seeking), and social (e.g., peer influence)[46]. Given the complex interaction of these factors amore inclusive understanding of their relationship is

Page 7 of 9needed along with the implementation of multifaceted
prevention and intervention strategies. While PA andactive transportation play significant roles, other contextual and individual factors also affect substance use
among adolescents.
Most studies cited in the reviews focused on datafrom adolescents in North America and Europe [17]. In
contrast, our study includes global data from 66 countries, predominantly outside Europe and North America. This is a major strength of our study, along with its
large sample size. Furthermore, the data presented aresupported by high-quality standards endorsed by the
WHO and the responsible agencies in the participating
countries [20] .
There are, however, some limitations of this study.
Being cross-sectional, it cannot establish causal relationships or observe changes over time. Additionally,
the use of self-report questionnaires may introduce bias.
Nevertheless, there is evidence that questionnaires haveacceptable validity for measuring adherence to PA recommendations in adolescents, showing moderate to highcorrelation with accelerometers, which are considered
the gold standard for measuring PA in free-living environments [49]. This study also did not incorporate a hierarchical model in the analyses. Another limitation is thehigh amount of data excluded due to missing information(43%). Even though we chose not to include participants
with incomplete data the remaining sample remains
representative and suitable for our research objectives.
Despite this, generalization of the results should beapproached with caution.
Additional research, particularly longitudinal studies, therefore, is needed to explore the potential of PA
in preventing tobacco and alcohol use. Investigating factors that might divert adolescents from PA and lead themto substance use is also crucial. Further, future studies should examine potential regional differences in the
results of the models used.

Conclusion
PA was associated with reduced tobacco and alcohol use,
particularly with binge drinking and drunkenness. Evenadolescents who engaged in PA below the recommended
levels exhibited lower use of these substances comparedto those who were inactive.
Public policies aimed at reducing the use of psychoactive substances, such as tobacco and alcohol, should promote PA among children and adolescents. Encouraging
involvement in PA and creating environments that support an active lifestyle should be integral componentsof strategies to combat tobacco and alcohol use among
adolescents.

de Victo et al. BMC Pediatrics(2024) 24:596

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12887-​024-​05079-1.
Supplementary Material 1.
Acknowledgements
We would like to thank the World Health Organization (WHO) and the Centers for Disease Control and Prevention (US CDC) for data produced with
the Global School Health Survey, providing studies with data from low- andmiddle-income countries. This document uses data from the Global schoolbased student health survey (GSHS). The GSHS is maintained by the World
Health Organization and the US Centers for Disease Control and Prevention.
Authors’ contributions
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, visualization: ERV, GF, CD and DS. All authors have read andagreed to the published version of the manuscript.
Funding
This study was financed in part by the Coordenação de Aperfeiçoamento dePessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
Availability of data and materials
This document uses data from the Global School-Based Student Health
Survey (GSHS). The GSHS is maintained by the World Health Organization andthe United States Centers for Disease Control and Prevention. All data fromthis study can be found on the website: https://​www.​who.​int/​teams/​nonco​
mmuni​cable-​disea​ses/​surve​illan​ce/​syste​ms-​tools/​global-​school-​based-​stude​
nt-​health-​survey.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Disciplina de Alergia, Imunologia Clínica e Reumatologia do Departamento
de Pediatria da Universidade Federal de São Paulo (UNIFESP), São Paulo, Brasil.
2
Universidad de Santiago de Chile (USACH), Escuela de Ciencias de la Actividad Física, el Deporte y la Salud, Santiago, Chile. 3 Facultad de Ciencias de la
Salud, Universidad Autónoma de Chile, Providencia, Santiago, Chile. 4 Divisionof Sport, Physical Activity and Health, University of Education Upper Austria,
Linz 4020, Austria.
Received: 12 April 2024 Accepted: 12 September 2024

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