Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2015 Mar 23;26(3):432–442. doi: 10.1111/jora.12203

Associations of Physical Activity and Sedentary Behavior With Adolescent Academic Achievement

Marko T Kantomaa 1,2,✉, Emmanuel Stamatakis 3,4, Anna Kankaanpää 1, Eero Kajantie 5,6,7, Anja Taanila 8, Tuija Tammelin 1
PMCID: PMC4995724  PMID: 27610027

Abstract

The Northern Finland Birth Cohort 1986 (N = 8,061) was used to investigated the joint associations of physical activity and sedentary behavior with academic achievement. Logistic regression analysis was used to evaluate how classes formed by latent class analysis (LCA) according to overall physical activity, sports club membership, viewing TV, using a computer, reading books and magazines, other sedentary activities, and sleep were associated with grade‐point average. When adjusted for gender, self‐rated health, and mother's education, physically active adolescents and generally active adolescents were about twice as likely to have high grade‐point average compared with sedentary TV viewers.

Only one‐third of youth worldwide are estimated to be sufficiently physically active (Ekelund, Tomkinson, & Armstrong, 2011), whereas sedentary behavior has increased during the last few decades; today, young people are spending 4–8 hr sedentary per day (Pate, Mitchell, Byun, & Dowda, 2011). Besides its well‐known benefits on both physical and mental health (Mountjoy et al., 2011), adequate physical activity may also benefit academic achievement in children and youth (Burkhalter & Hillman, 2011; Rasberry et al., 2011; Singh, Uijtdewilligen, Twisk, van Mechelen, & Chinapaw, 2012). Physical activity may enhance psychological functioning (Chaddock‐Heyman et al., 2013; Demirakca et al., 2014; Kalak et al., 2012), attention span and concentration (Coe, Pivarnik, Womack, Reeves, & Malina, 2006), self‐concept and academic self‐confidence (Coatsworth & Conroy, 2007), and, therefore, academic achievement. In addition, higher levels of aerobic fitness have been linked to learning and memory, especially in challenging learning situations (Raine et al., 2013).

In contrast, the increasing level of sedentary behavior has recently raised concerns as a growing education and public health problem (Burkhalter & Hillman, 2011; Schmidt & Vandewater, 2008). For example, media use (Mössle, Kleimann, Rehbein, & Pfeiffer, 2010; Sharif & Sargent, 2006), especially time spent viewing TV (Ennemoser & Schneider, 2007; Espinoza, 2009; Johnson, Cohen, Kasen, & Brook, 2007; Shin, 2004), playing video games (Jackson, von Eye, Witt, Zhao, & Fitzgerald, 2011), and using the Internet (Kim & So, 2012), has been linked to poor academic achievement in children and youth. Excessive screen time may displace comparable activities involving learning opportunities (e.g., doing homework and reading books), lead to mental idleness and passivity (especially TV viewing), and increase impulsive behavior, leading to attention difficulties and academic underachievement (Shin, 2004). Sensation seeking incompatible with concentrated efforts such as reading and writing, learning difficulties, negative attitudes toward school, substance use, and behavioral problems may also explain the association between media use and academic achievement (Johnson et al., 2007; Sharif, Wills, & Sargent, 2010).

Still, not all sedentary behavior is bad for academic achievement; for example, educational TV viewing (Kirkorian, Wartella, & Anderson, 2008; Schmidt & Vandewater, 2008), computer use (Subrahmanyam, Kraut, Greenfield, & Gross, n.d.), and reading books and newspapers (Sullivan & Brown, 2013) have been associated with favorable academic achievement. Today, more traditional forms of sedentary behavior, such as reading books, watching TV, and listening to radio, are intersecting with digital media and online communication (Jenkins, 2009), being a pervasive part of the everyday lives of youth (Ito, 2013). However, adolescents may differ vastly according to the amount they engage in and the way they combine, shape, and structure their physical activity and sedentary behavior in everyday life, which may hold the key to the prevention of the detrimental effects of sedentary behavior, and to the promotion of the benefits of physical activity on learning and academic achievement. To our knowledge, no previous studies have investigated the joint association of physical activity and sedentary behavior with adolescent academic achievement.

This cross‐sectional study investigated the joint association of physical activity, sports club membership, TV viewing, reading books or magazines, playing or working on a computer and playing video games, and other sedentary activities with academic achievement in adolescence. We hypothesized that a high level of physical activity, combined with a reasonable time spent in sedentary behavior, is positively associated with academic achievement.

Method

Participants

The study sample consisted of a prospective mother–child birth cohort, the Northern Finland Birth Cohort 1986 (NFBC 1986), which at the baseline was composed of 9,432 infants who were born alive and whose expected date of birth was between July 1, 1985, and June 30, 1986, in the two northernmost provinces of Finland, Oulu and Lapland (Järvelin, Hartikainen‐Sorri, & Rantakallio, 1993). In 2001–2002, at the age of 15–16 years (hereafter referred to as “16 years”), adolescents were sent a postal questionnaire that included questions about their physical activity and sedentary behavior (response rate 80%, N = 7,344). Information on academic achievement at the age of 16 was obtained from the National Application Register for Upper Secondary Education, which contains information on all adolescents in Finland. The present analysis included children with complete information on academic achievement at the age of 16; it excluded children with parental‐reported general learning disability, leaving a final study group of 8,061 children (4,126 boys and 3,935 girls). Informed consent was obtained from all participants and their parents, and the research protocol was approved by the Ethics Committee of Northern Ostrobothnia Hospital District.

Academic Achievement

Information on academic achievement was provided by the National Application Register for Upper Secondary Education, Finland, based on nationally comparable grades of the final assessment of basic education. The grades refer to a numerical assessment on a scale of 4–10, where 4 denotes a failure (US grade F) and 10 denotes excellent knowledge and skills (US grade A). They describe the level of performance in relation to the objectives of basic education at the end of Grade 9 (age 16). The grade‐point average (GPA) was calculated as a measure of academic achievement, based on grades obtained in the following school subjects: mother tongue (in most cases Finnish or Swedish), first foreign language (started in Grade 3), second national language (started in Grade 7), mathematics, biology, geography, physics, chemistry, religion or ethics, history, music, visual arts, physical education, crafts, and home economics. High academic achievement was defined as GPA ≥ 8.5 (the highest quartile of boys and girls) on a scale of 4.0–10.0. In Finnish basic education, an assessment grade of 8 denotes good, 9 denotes very good, and 10 denotes excellent performance. Finnish GPA 5.0–5.9 equals to 1.0 (D) in US GPA, 6.0–6.9 equals 2.0 (C), 7.0–8.9 equals 3.0 (B), and 9.0–10.0 equals 4.0 (A).

Physical Activity and Sedentary Behavior

Self‐reported physical activity outside school hours was evaluated separately for moderate‐to‐vigorous physical activity and light physical activity at the age of 16 by asking participants, “How many hours a week all together do you participate in (a) brisk and (b) light physical activity outside school hours?” In the questionnaire, the term brisk was defined as physical activity causing at least some sweating and shortness of breath (here referred to as moderate‐to‐vigorous intensity physical activity), while the term light physical activity was defined as causing no sweating or shortness of breath. In addition, the adolescents were asked about their time spent daily in physically active commutes to and from school. The response alternatives (not at all, less than 20 min, 20–39 min, 40–59 min, and at least 1 hr per day) were multiplied by five (5 school days a week) to correspond to 0, 1, 2.5, 3.75, and 5 hr per week (Tammelin, Ekelund, Remes, & Näyhä, 2007). The physical activity level was converted into metabolic equivalent of task (MET) hours per week, based on the intensity and the volume of physical activity engaged in outside school hours, including commuting to and from school (in this study, referred to as physical activity). In the calculations, a MET intensity value of three METs was used for light physical activity, five METs for brisk physical activity, and four METs for commuting physical activity (Ainsworth et al., 2011). MET hours per week were further divided into gender‐specific thirds: (1) high (the highest tertile), (2) average (the middle tertile), and (3) low (the lowest tertile). These physical activity questions have good test–retest reliability when administered to Finnish adolescents aged 15–16 years (Tammelin et al., 2007). The intraclass correlation coefficient for physical activity levels described in terms of quintile categories of MET hours per week was .70 (95% confidence interval .58–.80), and the proportion of subjects who were classified in exactly the same category, or next to the same category, in two different tests was 86%.

Participants were also asked about membership and participation in training sessions of a sports club. For the analyses, the adolescents were categorized as sports club members if they reported being members of a sports club, regardless of their participation in the training sessions.

Self‐reported sedentary behavior was evaluated by asking the participants, “How many hours a day on average do you spend in the following activities outside school hours: (1) TV viewing, (2) reading books or magazines, (3) playing or working on a computer and playing video games, and (4) on other sedentary activities?” (Tammelin et al., 2007). Adolescents were classified into three groups according to the time spent on these activities: (1) <1 hr per day, (2) 1–2 hr per day, (3) >2 hr per day (Tammelin et al., 2007). Quantity of sleep at the age of 16 years was evaluated in the postal questionnaire by asking the adolescents how many hours on average they spent sleeping per day and was categorized as: (1) <8 hr per day, (2) 8 hr per day, and (3) >8 hr per day.

Potential Covariates

Potential covariates were selected based on our subject matter knowledge (e.g., Kantomaa et al., 2013) and statistical testing. As we had complete information on education level from larger number of mothers compared to fathers, we selected maternal education in the present models. The mother's highest level of education when the adolescent was 16 years was ascertained from the parents. The variable was categorized according to educational level categories used by the International Standard Classification of Education: (1) basic education, lasting 9 years or less; (2) upper secondary education, lasting 10–12 years; and (3) tertiary education, lasting ≥13 years (Statistics Finland, 2001).

Self‐rated overall health was measured by asking, “How would you describe your health at the moment?” The response alternatives were as follows: (1) very poor, (2) poor, (3) fair, (4) good, and (5) very good.

Statistical Analyses

Multivariable logistic regression analysis was used to evaluate how classes formed by latent class analysis (LCA), according to self‐reported physical activity, sports club membership duration, and sedentary behaviors, were associated with high GPA. The number of latent classes was approximated by using Akaike's information criterion, Bayesian information criterion (BIC), and sample‐size adjusted BIC. For LCA models with categorical outcomes, the adjusted BIC correctly identifies the number of classes more consistently when compared to other information criteria (Nylund, Asparouhov, & Muthén, 2007). The statistical tests Vuong‐Lo‐Mendell‐Rubin likelihood ratio test, Lo‐Mendell‐Rubin adjusted likelihood ratio test, and parametric bootstrapped likelihood ratio test (BLRT) were applied for determining the number of clusters as well. Entropy was used to evaluate the classification quality. For the further analysis, subjects were classified into their most likely classes. The results of the regression analyses are presented with odds ratios (OR) and 95% confidence intervals [95% CI]. In the multivariable models, the variables were adjusted for gender, self‐rated health, and mother's highest level of education. Full information maximum likelihood estimation with robust standard errors was used under the assumption of data missing at random. The SPSS® 19.0 (IBM Corporation, Armonk, NY, United States) for Windows statistical package (SPSS, 2010) was used for calculating the descriptive statistics and the Mplus statistical package (version 7) (Muthén & Muthén, 2012) was used for latent class analysis (LCA) and multivariable logistic regression analyses.

Results

At the age of 16, girls (M = 8.1, SD = 0.8) had higher academic achievement levels, as indicated by GPA, compared with boys (M = 7.5, SD = 0.9), p < .001. The mean metabolic equivalent of task (MET) hours per week were 32.8 (SD = 17.9) for boys and 28.7 (SD = 15.5) for girls, p < .001. The gender‐specific distributions of categorized teacher‐reported academic achievement (GPA), self‐reported physical activity (MET hours per week) and sedentary behavior (TV viewing, reading books or magazines, playing or working on a computer and playing video games, and other sedentary activities), and all contextual variables from the original data are presented in Table 1.

Table 1.

Characteristics of the Northern Finland Birth Cohort 1986, 2001–2002

Characteristics Males (n = 4,126) Females (n = 3,935) All (N = 8,061) p‐Value a
n (%) n (%) n (%)
Academic achievementb 4,065 3,902 7,967
Average or lower 3,488 (85.8) 2,510 (64.3) 5,998 (5.3)
Good 577 (14.2) 1,392 (35.7) 1,969 (24.7) <.001
Physical activityc 3,093 3,305 6,398
Low 871 (28.2) 1,116 (33.8) 1,987 (31.1)
Average 953 (30.8) 1,227 (37.1) 2,180 (34.1)
High 1,269 (41.0) 962 (29.1) 2,231 (34.9) <.001
Sports club membership 3,122 3,336 6,458
No 2,103 (67.4) 2,491 (74.7) 4,594 (71.1)
Yes 1,019 (32.6) 845 (25.3) 1,864 (28.9) <.001
TV viewing 2,956 3,216 6,172
<1 hr/day 395 (13.4) 445 (13.8) 840 (13.6)
1–2 hr/day 1,225 (41.4) 1,442 (44.8) 2,667 (43.2)
>2 hr/day 1,336 (45.2) 1,329 (41.3) 2,665 (43.2) .008
Computer use/video games 2,995 3,307 6,302
<1 hr/day 583 (19.5) 2,011 (60.8) 2,594 (32.2)
1–2 hr/day 1,788 (59.7) 1,208 (36.5) 2,996 (37.2)
>2 hr/day 624 (20.8) 88 (2.7) 712 (8.8) <.001
Reading books or magazines 3,098 3,321 6,419
<1 hr/day 1,054 (34.0) 741 (22.3) 1,795 (28.0)
1–2 hr/day 1,906 (61.5) 2,294 (69.1) 4,200 (65.4)
>2 hr/day 138 (4.5) 286 (8.6) 424 (6.6) <.001
Other sedentary behavior 2,840 3,016 5,856
<1 hr/day 866 (30.5) 668 (22.1) 1,534 (26.2)
1–2 hr/day 1641 (57.8) 1,761 (58.4) 3,402 (58.1)
>2 hr/day 333 (11.7) 587 (19.5) 920 (15.7) <.001
Sleeping 3,109 3,097 6,454
<8 hr/day 510 (16.4) 809 (24.2) 1,319 (20.4)
8 hr/day 1,290 (41.5) 1,482 (44.3) 2,772 (43.0)
>8 hr/day 1,309 (42.1) 1,054 (31.5) 2,363 (36.6) <.001
Mother's education 3,104 3,097 6,201
Basic/upper secondary 2,691 (86.7) 2,698 (87.1) 5,389 (86.9)
Higher 413 (13.3) 399 (12.9) 812 (13.1) .622
Self‐rated health 3,134 3,357 6,520
Very poor 5 (0.2) 3 (0.1) 8 (0.1)
Poor 23 (0.7) 38 (1.1) 61 (0.9)
Fair 423 (13.5) 527 (15.7) 955 (14.7)
Good 1,751 (55.9) 2,119 (63.1) 3,889 (59.7)
Very good 932 (29.7) 670 (20.0) 1,607 (24.6) <.001
Open in a new tab

GPA = grade‐point average.

a

p‐Values for the gender differences (Pearson's chi‐squared test).

b

GPA (scale 4.0–10.0) included mother tongue (in most cases, Finnish or Swedish), first foreign language (started at Grade 3), second national language (started at Grade 7), mathematics, biology, geography, physics, chemistry, religion or ethics, history, music, visual arts, physical education, crafts, and home economics. Good academic achievement was classified as GPA ≥ 8.5.

c

The physical activity level was defined as metabolic equivalent hours per week based on the intensity and volume of physical activity engaged in outside school hours, including commuting to and from school, and was divided into gender‐specific thirds: (1) high (the highest tertile), (2) average (the middle tertile), and (3) low (the lowest tertile).

High levels of physical activity (OR = 1.58, 95% CI [1.36, 1.84]) and sports club membership (OR = 1.85, 95% CI [1.64, 2.16]) were associated with good academic achievement at the age of 16 (Table 2). Furthermore, reasonable time (<1 hr per day vs. >2 hr per day) spent viewing TV (OR = 1.87, 95% CI [1.56, 2.23]) and playing or working on a computer and playing video games (OR = 1.36, 95% CI [1.06, 1.73]) were associated with good academic achievement, compared with higher amounts of sedentary behaviors. In addition, spending 1–2 hr per day in other sedentary behaviors (OR = 1.35, 95% CI [1.14, 1.61]) and sleeping for 8 hr per day (OR = 1.48, 95% CI [1.26, 1.74]), were associated with good academic achievement, compared with higher levels of other sedentary behaviors and sleeping for less than 8 hr per day, respectively.

Table 2.

Logistic Regression of Good Academic Achievement and the Level of Physical Activity and Sedentary Behavior

n Good Academic Achievementa
% Adjustedb OR [95% CI]
Physical activityc 6,398
Low 1,987 21.9 1.00
Average 2,180 29.6 1.44 [1.24, 1.67]
High 2,231 30.3 1.58 [1.36, 1.84]
Sports club membership 6,458
No 4,594 71.1 1.00
Yes 1,864 28.9 1.75 [1.54, 1.99]
TV viewing 6,172
>2 hr/day 2,665 21.5 1.00
1–2 hr/day 2,667 32.7 1.68 [1.47, 1.90]
<1 hr/day 840 35.6 1.87 [1.56, 2.23]
Computer use/video games 6,302
>2 hr/day 2,594 16.2 1.00
1–2 hr/day 2,996 23.7 1.11 [.88, 1.40]
<1 hr/day 712 35.3 1.36 [1.06, 1.73]
Reading books or magazines 6,419
>2 hr/day 424 31.2 1.00
1–2 hr/day 4,200 29.2 1.05 [.83, 1.32]
<1 hr/day 1,795 22.4 .85 [.66, 1.09]
Other sedentary behavior 5,856
>2 hr/day 920 25.5 1.00
1–2 hr/day 3,402 29.6 1.35 [1.14, 1.61]
<1 hr/day 1,534 26.6 1.25 [1.03, 1.53]
Sleeping 6,454
<8 hr/day 1,319 24.0 1.00
8 hr/day 2,772 31.4 1.48 [1.26, 1.74]
>8 hr/day 2,363 24.8 1.16 [.98, 1.37]
Open in a new tab

GPA = grade‐point average; OR = odds ratios; 95% CI = 95% confidence intervals.

(N = 8,061).

a

GPA (scale 4.0–10.0) included mother tongue (in most cases, Finnish or Swedish), first foreign language (started at Grade 3), second national language (started at Grade 7), mathematics, biology, geography, physics, chemistry, religion or ethics, history, music, visual arts, physical education, crafts, and home economics. Good academic achievement was classified as GPA ≥ 8.5.

b

Adjusted for gender, self‐rated health, and mother's level of education.

c

The physical activity level was defined as metabolic equivalent hours per week based on the intensity and volume of physical activity engaged in outside school hours, including commuting to and from school, and was divided into gender‐specific thirds: (1) high (the highest tertile), (2) average (the middle tertile), and (3) low (the lowest tertile).

Latent class analysis models were fitted to the seven physical activity and sedentary behaviors reported above. Information criteria and statistical tests, except for the BLRT, indicated that the number of classes would be 4–6 (Table 3). Based on the lowest value of the adjusted BIC, the five‐class solution was chosen. Entropy for this solution was .60. Figure 1 presents the response probability profiles for each of the five classes (C1–C5). The first class (C1: sedentary TV viewers) was characterized by watching TV for more time, spending less time in other sedentary activities, having low overall physical activity, and not being members of a sports club. Adolescents in class 2 (C2: generally inactive) had low overall physical activity, were rarely members of a sports club, and spent little time in sedentary activities. The third class (C3: moderately active readers) was characterized by having a moderate level of overall physical activity, rarely being members of a sports club, spending a fair bit of time reading books and magazines, but only a little time on other sedentary behavior, especially watching TV. Adolescents in class 4 (C4: active sports clubbers) had a high overall level of physical activity, were often members of a sports club, and spent a fair amount of time using various media. The fifth class (C5: generally active) were physically active, were often members of a sports club, and spent little time in sedentary activities. Adolescents in different classes did not have significant differences according to quantity of sleep (Figure 1).

Table 3.

Classes Identified Through Latent Class Analysis (LCA) with Goodness‐of‐Fit Statistics

Number of classes AIC BIC Adjusted BIC VLMR LMR BLRT Entropy
2 81,895 82,092 82,000 <.001 <.001 <.001 .91
3 81,673 81,972 81,832 .0003 .0003 <.001 .59
4 81,515 81,915 81,728 .0007 .0008 <.001 .57
5 81,444 81,945 81,710 1.0000 1.0000 <.001 .60
6 81,413 82,017 81,734 1.0000 1.0000 <.001 .56
Open in a new tab

AIC = Akaike's information criterion; BIC = Bayesian information criterion; VLMR = Vuong‐Lo‐Mendell‐Rubin likelihood ratio test; LMR = Lo‐Mendell‐Rubin adjusted likelihood ratio test; BLRT = parametric bootstrapped likelihood ratio test.

(n = 6,509).

Figure 1.

Figure 1

Open in a new tab

Characteristics of the classes (C1–C5) formed by physical activity and different forms of sedentary behavior at the age of 16 years (n = 6,509). The colors denote percentages of the class sample in low (light gray), medium (dark gray), and high (black) levels of activity. M = male; F = female.

Adolescents in classes C3 (moderately active readers), C4 (active sports clubbers), and C5 (generally active) more commonly (35% vs. 22%) had high GPA compared with adolescents in classes C1 (sedentary TV viewers) and C2 (generally inactive) (Table 4). When adjusted for gender, self‐rated health, and mother's education, generally active adolescents (OR = 2.02, 95% CI [1.65, 2.48]) and active sports clubbers (OR = 1.98, 95% CI [1.68, 2.33]) were about twice as likely to have high GPA as compared with sedentary TV viewers. In addition, moderately active readers (OR = 1.81, 95% CI [1.51, 2.18]) were almost twice as likely to have high GPA as compared with sedentary TV viewers. There was not a statistically significant difference according to academic achievement between sedentary TV viewers and generally inactive adolescents (Table 4).

Table 4.

Logistic Regression of Good Academic Achievement and the Classes Identified Through Latent Class Analysis (LCA)

n Good Academic Achievementa
% Unadjusted OR [95% CI] Adjustedb OR [95% CI]
Class 1 (sedentary TV viewers) 2,937 22.4 1.00 1.00
Class 2 (generally inactive) 1,058 22.3 .99 [.84, 1.18] 1.02 [.85, 1.22]
Class 3 (moderately active readers) 778 35.0 1.87 [1.57, 2.21] 1.81 [1.51, 2.18]
Class 4 (active sports clubbers) 1,132 35.0 1.86 [1.60, 2.16] 1.98 [1.68, 2.33]
Class 5 (generally active) 604 34.7 1.85 [1.53, 2.23] 2.02 [1.65, 2.48]
Open in a new tab

GPA = grade‐point average; OR = odds ratios; 95% CI = 95% confidence intervals.

(N = 8,061).

a

GPA (scale 4.0–10.0) included mother tongue (in most cases, Finnish or Swedish), first foreign language (started at Grade 3), second national language (started at Grade 7), mathematics, biology, geography, physics, chemistry, religion or ethics, history, music, visual arts, physical education, crafts, and home economics. Good academic achievement was classified as GPA ≥ 8.5.

b

Adjusted for gender, self‐rated health, and mother's level of education.

Discussion

In this study, a high level of physical activity, with a reasonable amount of time spent in sedentary behavior, such as media use, was associated with good academic achievement, whereas a high level of sedentary behavior, especially TV viewing, was associated with lower levels of academic achievement in adolescence.

Our finding of a positive association between physical activity and academic achievement is consistent with previous studies concluding that self‐reported physical activity is associated with good academic achievement in adolescents (Fox, Barr‐Anderson, Neumark‐Sztainer, & Wall, 2010; Kantomaa et al., 2013; Nelson & Gordon‐Larsen, 2006). Physical activity may enhance brain health, structure, and function (Chaddock‐Heyman et al., 2013; Demirakca et al., 2014; Hillman, Erickson, & Kramer, 2008); induce arousal and reduce boredom, leading to increased attention span and concentration (Coe et al., 2006); and improve self‐concept and academic self‐confidence (Coatsworth & Conroy, 2007). Furthermore, physical activity may enhance young people's cognitive functions, such as executive functions and working memory, and, therefore, academic achievement (Castelli, Hillman, Hirsch, Hirsch, & Drollette, 2011; Chaddock, Hillman, Buck, & Cohen, 2011; Davis et al., 2011).

Our results support previous studies reporting that participation in organized physical activity is positively associated with academic achievement (Rasberry et al., 2011). This finding may have special significance in light of the increase in participation in organized youth sports over the past decade (Myer et al., 2011). It has been proposed that participation in organized sports fosters student identification with school and school‐related values, including performing well academically (Fox et al., 2010). Participation in organized physical activities may also develop young people's ability to listen to and follow instructions, to wait for their own turn, and to choose ways of action appropriate for the situation. Furthermore, physical activity may provide young people with opportunities to vent and process their feelings (Kantomaa, Tammelin, Ebeling, & Taanila, 2008). At its best, engaging in organized physical activities develops teamwork skills, self‐direction, and the ability to cooperate with different people (Kantomaa et al., 2008). All of these aspects may contribute to explaining the good academic achievement of adolescents participating in organized physical activities.

Our results are in line with previous findings showing that reasonable time spent in sedentary behavior, such as media use, is positively associated with academic achievement (Schmidt & Vandewater, 2008; Williams, Haertel, Haertel, & Walberg, 1982). However, it seems that the context of the media use and the content delivered by the media are more influential than the media themselves (Borzekowski & Robinson, 2005; Schmidt & Vandewater, 2008). For example, viewing educational TV is positively linked with academic achievement, whereas viewing entertainment TV is negatively linked with achievement (Kirkorian et al., 2008; Schmidt & Vandewater, 2008). Furthermore, a recent study reported that video gaming induces structural brain plasticity, which may benefit cognitive functions such as working memory, behavioral flexibility, attention, and future planning (Kühn, Gleich, Lorenz, Lindenberger, & Gallinat, 2014) and, therefore, academic achievement (Castelli et al., 2011; Chaddock et al., 2011; Davis et al., 2011).

Today, more traditional media, such as books, television, and radio, are intersecting with digital media (Jenkins, 2009), providing young people with more opportunities for online social interaction, problem solving, and self‐directed activity that lead to diverse forms of learning in formal and informal settings (Ito, 2013). The present study demonstrates that, when combined with the various benefits of physical activity on learning and cognitive function, digital and media technology‐mediated learning may provide adolescents with skills and abilities that best benefit academic achievement in today's complex and ubiquitous learning environments. However, it should be pointed out that in 2012 in the United States, for example, 95% of U.S. adolescents reported using the Internet, compared to 87% in 2004, and 73% in 2000 (Madden, Lenhart, Duggan, Cortesi, & Gasser, n.d.), 1 year before the start of the present data collection (2001–2002). Also the use of digital media among adolescents has changed considerably during the past decade, for example, from stationary connections to always‐on (mobile) connections that move with the user throughout the day (Madden et al., n.d.). It is possible that the association between media use and academic achievement would be slightly different in current data. On the other hand, today, increasing mobile connectivity may provide young people with whole new opportunities for combining physically active lifestyle, media use, and technology‐enhanced learning.

Our study was based on a large, unselected population sample with a high participation rate (80%). Information on academic achievement was based on nationally comparable grades of the final assessment of basic education obtained from the National Application Register for Upper Secondary Education, providing a representative and comprehensive estimate of adolescents' academic achievement. In addition, we included various measures of physical activity and sedentary behavior in our models and were able to provide a comprehensive picture of these behaviors within various activity domains. However, we did not have information on the content of the media delivered, which may be crucial for the influence of sedentary behavior on academic achievement. The cross‐sectional setting of the present study does not allow us to draw strong conclusions about causality between physical activity, sedentary behavior, and academic achievement. Furthermore, our study relied on adolescents' self‐reported physical activity and sedentary behavior, which might contain relatively large measurement errors and social desirability bias (Sallis & Saelens, 2000; Shephard, 2003). Finally, the present data was collected in 2001–2002, limiting the interpretation of the results in light of the current social environment with most adolescents having pervasive access to Internet, more commonly through mobile devices (Madden et al., n.d.). Nonetheless, our study sample represents a generation considered “digital natives” (Prensky, 2001) and it may therefore be a reasonable reflection of the younger generation with respect to media use.

The results of the present study form a basis for future research investigating the possible causality between physical activity, sedentary behavior, and academic achievement. It would be useful to investigate the associations in more contemporary cohorts, and within different sociocultural settings. Better identification of mediating and moderating variables would be especially beneficial for physical activity interventions, which could be targeted at improving these behaviors, as well as academic outcomes. It is also possible that the joint association of physical activity and sedentary behavior with academic achievement varies according to the forms of these activities, offering interesting viewpoints for future studies.

In conclusion, a high level of physical activity and a reasonable time spent in sedentary behavior, such as media use, were associated with good academic achievement, whereas a high amount of sedentary behavior, especially TV viewing, was associated with lower levels of academic achievement in adolescence. In today's complex and dynamic learning environments, physical activity combined with reasonable time using multiple media may provide young people with the skills and abilities that will best benefit their academic achievement.

MTK is supported by the Ministry of Education and Culture, Finland, grant 23/626/2012. AK and TT are supported by Academy of Finland, grant 273971. The Northern Finland Birth Cohort 1986 is supported by grants from the Academy of Finland, European Union, National Institutes of Health, Wellcome Trust, Nordic Academy for Advanced Study, and Ministry of Education and Culture, Finland.

References

  1. Ainsworth, B. E. , Haskell, W. L. , Herrmann, S. D. , Meckes, N. , Bassett, D. R. , Tudor‐Locke, C. , … Leon, A. S. (2011). 2011 Compendium of physical activities: A second update of codes and MET values. Medicine and Science in Sports and Exercise, 43, 1575–1581. doi:10.1249/MSS.0b013e31821ece12 [DOI] [PubMed] [Google Scholar]
  2. Borzekowski, D. L. G. , & Robinson, T. N. (2005). The remote, the mouse, and the no. 2 pencil: The household media environment and academic achievement among third grade students. Archives of Pediatrics and Adolescent Medicine, 159, 607–613. doi:10.1001/archpedi.159.7.607 [DOI] [PubMed] [Google Scholar]
  3. Burkhalter, T. M. , & Hillman, C. H. (2011). A narrative review of physical activity, nutrition, and obesity to cognition and scholastic performance across the human lifespan. Advances in Nutrition, 2, 201S–206S. doi:10.3945/an.111.000331 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Castelli, D. M. , Hillman, C. H. , Hirsch, J. , Hirsch, A. , & Drollette, E. (2011). FIT Kids: Time in target heart zone and cognitive performance. Preventive Medicine, 52(Suppl. 1), S55–S59. doi:10.1016/j.ypmed.2011.01.019 [DOI] [PubMed] [Google Scholar]
  5. Chaddock, L. , Hillman, C. H. , Buck, S. M. , & Cohen, N. J. (2011). Aerobic fitness and executive control of relational memory in preadolescent children. Medicine and Science in Sports and Exercise, 43, 344–349. doi:10.1249/MSS.0b013e3181e9af48 [DOI] [PubMed] [Google Scholar]
  6. Chaddock‐Heyman, L. , Erickson, K. I. , Voss, M. W. , Knecht, A. M. , Pontifex, M. B. , Castelli, D. M. , Hillman, C. H. , & Kramer, A. F. (2013). The effects of physical activity on functional MRI activation associated with cognitive control in children: A randomized controlled intervention. Frontiers in Human Neuroscience, 7, 72. doi:10.3389/fnhum.2013.00072 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Coatsworth, J. D. , & Conroy, D. E. (2007). Youth sport as a component of organized afterschool programs. New Directions for Youth Development, 115, 57–74. doi:10.1002/yd.223 [DOI] [PubMed] [Google Scholar]
  8. Coe, D. P. , Pivarnik, J. M. , Womack, C. J. , Reeves, M. J. , & Malina, R. M. (2006). Effect of physical education and activity levels on academic achievement in children. Medicine and Science in Sports and Exercise, 38, 1515–1519. doi:10.1249/01.mss.0000227537.13175.1b [DOI] [PubMed] [Google Scholar]
  9. Davis, C. L. , Tomporowski, P. D. , McDowell, J. E. , Austin, B. P. , Miller, P. H. , Yanasak, N. E. , Allison, J. D. , & Naglieri, J. A. (2011). Exercise improves executive function and achievement and alters brain activation in overweight children: A randomized, controlled trial. Health Psychology, 91–98. doi:10.1037/a0021766 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Demirakca, T. , Brusniak, W. , Tunc‐Skarka, N. , Wolf, I. , Meier, S. , Matthäus, F. , … Diener, C. (2014). Does body shaping influence brain shape? Habitual physical activity is linked to brain morphology independent of age The World Journal of Biological Psychiatry, 15, 387–396. doi:10.3109/15622975.2013.803600 [DOI] [PubMed] [Google Scholar]
  11. Ekelund, U. , Tomkinson, G. , & Armstrong, N. (2011). What proportion of youth are physically active? Measurement issues, levels and recent time trends. British Journal of Sports Medicine, 45, 859–865. doi:10.1136/bjsports‐2011‐090190 [DOI] [PubMed] [Google Scholar]
  12. Ennemoser, M. , & Schneider, W. (2007). Relations of television viewing and reading: Findings from a 4‐year longitudinal study. Journal of Educational Psychology, 99, 349–368. doi:10.1037/0022‐0663.99.2.349 [Google Scholar]
  13. Espinoza, F. (2009). Using project‐based data in physics to examine television viewing in relation to student performance in science. Journal of Science Education and Technology, 18, 458–465. doi:10.1007/s10956‐009‐9163‐5 [Google Scholar]
  14. Fox, C. K. , Barr‐Anderson, D. , Neumark‐Sztainer, D. , & Wall, M. (2010). Physical activity and sports team participation: Associations with academic outcomes in middle school and high school students. The Journal of School Health, 80, 31–37. doi:10.1111/j.1746‐1561.2009.00454.x [DOI] [PubMed] [Google Scholar]
  15. Hillman, C. H. , Erickson, K. I. , & Kramer, A. F. (2008). Be smart, exercise your heart: Exercise effects on brain and cognition. Nature Reviews. Neuroscience 9, 58–65. doi:10.1038/nrn2298 [DOI] [PubMed] [Google Scholar]
  16. Ito, M. (2013). Hanging out, messing around, and geeking out: Kids living and learning with new media. Cambridge, MA: The MIT Press. [Google Scholar]
  17. Jackson, L. A. , von Eye, A. , Witt, E. A. , Zhao, Y. , & Fitzgerald, H. E. (2011). A longitudinal study of the effects of Internet use and videogame playing on academic performance and the roles of gender, race and income in these relationships. Computers in Human Behavior, 27, 228–239. doi:10.1016/j.chb.2010.08.001 [Google Scholar]
  18. Järvelin, M. R. , Hartikainen‐Sorri, A. L. , & Rantakallio, P. (1993). Labour induction policy in hospitals of different levels of specialisation. British Journal of Obstetrics and Gynaecology, 100, 310–315. doi:10.1111/j.1471‐0528.1993.tb12971.x [DOI] [PubMed] [Google Scholar]
  19. Jenkins, H. (2009). Confronting the challenges of participatory culture: media education for the 21st century. Cambridge, MA: The MIT Press. [Google Scholar]
  20. Johnson, J. G. , Cohen, P. , Kasen, S. , & Brook, J. S. (2007). Extensive television viewing and the development of attention and learning difficulties during adolescence. Archives of Pediatrics and Adolescent Medicine, 161, 480–486. doi:10.1001/archpedi.161.5.480 [DOI] [PubMed] [Google Scholar]
  21. Kalak, N. , Gerber, M. , Kirov, R. , Mikoteit, T. , Yordanova, J. , Pühse, U. , … Brand, S. (2012). Daily morning running for 3 weeks improved sleep and psychological functioning in healthy adolescents compared with controls. Journal of Adolescent Health, 51, 615–622. doi:10.1016/j.jadohealth.2012.02.020 [DOI] [PubMed] [Google Scholar]
  22. Kantomaa, M. T. , Stamatakis, E. , Kankaanpää, A. , Kaakinen, M. , Rodriguez, A. , Taanila, A. , … Tammelin, T. (2013). Physical activity and obesity mediate the association between childhood motor function and adolescents' academic achievement. Proceedings of the National Academy of Sciences of the United States of America, 110, 1917–1922. doi:10.1073/pnas.1214574110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kantomaa, M. T. , Tammelin, T. H. , Ebeling, H. E. , & Taanila, A. M. (2008). Emotional and behavioral problems in relation to physical activity in youth. Medicine and Science in Sports and Exercise, 40, 1749–1756. doi:10.1249/MSS.0b013e31817b8e82 [DOI] [PubMed] [Google Scholar]
  24. Kim, D.‐H. , & So, W.‐Y. (2012). The relationship between daily Internet use time and school performance in Korean adolescents. Central European Journal of Medicine, 7, 444–449. doi:10.2478/s11536‐012‐0019‐7 [Google Scholar]
  25. Kirkorian, H. L. , Wartella, E. A. , & Anderson, D. R. (2008). Media and young children's learning. The Future of Children/Center for the Future of Children, 18, 39–61. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21338005. doi:10.1111/j.1471‐0528.1993.tb12971.x [DOI] [PubMed] [Google Scholar]
  26. Kühn, S. , Gleich, T. , Lorenz, R. C. , Lindenberger, U. , & Gallinat, J. (2014). Playing Super Mario induces structural brain plasticity: Gray matter changes resulting from training with a commercial video game. Molecular Psychiatry, 19, 265–271. doi:10.1038/mp.2013.120 [DOI] [PubMed] [Google Scholar]
  27. Madden, M. , Lenhart, A. , Duggan, M. , Cortesi, S. , & Gasser, U. (n.d.). Teens and technology 2013. Retrieved from http://www.pewinternet.org/2013/03/13/teens-and-technology-2013/
  28. Mössle, T. , Kleimann, M. , Rehbein, F. , & Pfeiffer, C. (2010). Media use and school achievement–boys at risk? The British Journal of Developmental Psychology, 28(Pt. 3), 699–725. doi:10.1111/j.1471‐0528.1993.tb12971.x [DOI] [PubMed] [Google Scholar]
  29. Mountjoy, M. , Andersen, L. B. , Armstrong, N. , Biddle, S. , Boreham, C. , Bedenbeck, H.‐P. B. , … van Mechelen, W. (2011). International Olympic Committee consensus statement on the health and fitness of young people through physical activity and sport. British Journal of Sports Medicine, 45, 839–848. doi:10.1136/bjsports‐2011‐090228 [DOI] [PubMed] [Google Scholar]
  30. Muthén, L. , & Muthén, B. (2012). Mplus user's guide (7th ed.). Los Angeles, CA: Muthén & Muthén. [Google Scholar]
  31. Myer, G. D. , Faigenbaum, A. D. , Ford, K. R. , Best, T. M. , Bergeron, M. F. , & Hewett, T. E. (2011). When to initiate integrative neuromuscular training to reduce sports‐related injuries and enhance health in youth? Current Sports Medicine Reports, 10, 155–166. doi:10.1249/JSR.0b013e31821b1442 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Nelson, M. C. , & Gordon‐Larsen, P. (2006). Physical activity and sedentary behavior patterns are associated with selected adolescent health risk behaviors. Pediatrics, 117, 1281–1290. doi:10.1542/peds.2005‐1692 [DOI] [PubMed] [Google Scholar]
  33. Nylund, K. L. , Asparouhov, T. , & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14, 535–569. doi:10.1080/10705510701575396 [Google Scholar]
  34. Pate, R. R. , Mitchell, J. A. , Byun, W. , & Dowda, M. (2011). Sedentary behaviour in youth. British Journal of Sports Medicine, 45, 906–913. doi:10.1136/bjsports‐2011‐090192 [DOI] [PubMed] [Google Scholar]
  35. Prensky, M. (2001). Digital natives, digital immigrants Part 1. On the Horizon, 9, 1–6. doi:10.1108/10748120110424816 [Google Scholar]
  36. Raine, L. B. , Lee, H. K. , Saliba, B. J. , Chaddock‐Heyman, L. , Hillman, C. H. , & Kramer, A. F. (2013). The influence of childhood aerobic fitness on learning and memory. PLoS ONE, 8, e72666. doi:10.1371/journal.pone.0072666 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Rasberry, C. N. , Lee, S. M. , Robin, L. , Laris, B. A. , Russell, L. A. , Coyle, K. K. , & Nihiser, A. J. (2011). The association between school‐based physical activity, including physical education, and academic performance: A systematic review of the literature. Preventive Medicine, 52(Suppl. 1), S10–S20. doi:10.1016/j.ypmed.2011.01.027 [DOI] [PubMed] [Google Scholar]
  38. Sallis, J. F. , & Saelens, B. E. (2000). Assessment of physical activity by self‐report: Status, limitations, and future directions. Research Quarterly for Exercise and Sport, 71(2 Suppl.), S1–S14. doi:10.1111/j.1471‐0528.1993.tb12971.x [PubMed] [Google Scholar]
  39. Schmidt, M. E. , & Vandewater, E. A. (2008). Media and attention, cognition, and school achievement. The Future of Children/Center for the Future of Children, 18, 63–85. doi:10.1353/foc.0.0004 [DOI] [PubMed] [Google Scholar]
  40. Sharif, I. , & Sargent, J. D. (2006). Association between television, movie, and video game exposure and school performance. Pediatrics, 118, e1061–e1070. doi:10.1542/peds.2005‐2854 [DOI] [PubMed] [Google Scholar]
  41. Sharif, I. , Wills, T. A. , & Sargent, J. D. (2010). Effect of visual media use on school performance: A prospective study. Journal of Adolescent Health, 46, 52–61. doi:10.1016/j.jadohealth.2009.05.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Shephard, R. J. (2003). Limits to the measurement of habitual physical activity by questionnaires. British Journal of Sports Medicine, 37, 197–206. doi:10.1136/bjsm.37.3.197 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Shin, N. (2004). Exploring pathways from television viewing to academic achievement in school age children. The Journal of Genetic Psychology, 165, 367–381. doi:10.3200/GNTP.165.4.367‐382 [DOI] [PubMed] [Google Scholar]
  44. Singh, A. , Uijtdewilligen, L. , Twisk, J. W. R. , van Mechelen, W. , & Chinapaw, M. J. M. (2012). Physical activity and performance at school: A systematic review of the literature including a methodological quality assessment. Archives of Pediatrics and Adolescent Medicine, 166, 49–55. doi:10.1001/archpediatrics.2011.716 [DOI] [PubMed] [Google Scholar]
  45. Statistics Finland . (2001). UNESCO'S international Standard Classification of Education ISCED 1997: Finnish Implementation Manual 2000: Appendix 1 to the Finnish Standard Classification of Education 2000 (p. 169). Helsinki: Statistics Finland. [Google Scholar]
  46. Subrahmanyam, K. , Kraut, R. E. , Greenfield, P. M. , & Gross, E. F. (n.d.). The impact of home computer use on children's activities and development. The Future of Children/Center for the Future of Children, 10, 123–144. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11255703. doi:10.2307/1602692 [PubMed] [Google Scholar]
  47. Sullivan, A. , & Brown, M. (2013). Social inequalities in cognitive scores at age 16: The role of reading. London, UK: Centre for Longitudinal Studies. [Google Scholar]
  48. Tammelin, T. , Ekelund, U. , Remes, J. , & Näyhä, S. (2007). Physical activity and sedentary behaviors among Finnish youth. Medicine and Science in Sports and Exercise, 39, 1067–1074. doi:10.1249/mss.0b13e318058a603 [DOI] [PubMed] [Google Scholar]
  49. Williams, P. A. , Haertel, E. H. , Haertel, G. D. , & Walberg, H. J. (1982). The impact of leisure‐time television on school learning: A research synthesis. American Educational Research Journal, 19, 19–50. doi:10.3102/00028312019001019 [Google Scholar]

Articles from Journal of Research on Adolescence are provided here courtesy of Wiley

RESOURCES