ABSTRACT
Background
To better understand the key behavioral factors for adolescent obesity, this study investigated the independent and combined associations of physical activity (PA) and sedentary time (ST) with obesity prevalence across different school grades.
Methods
Cross‐sectional data from 614,943 adolescents in the Korea Youth Risk Behavior Web‐based Survey were analyzed. Independent associations of PA and ST with obesity were examined based on weekly PA frequency and daily nonacademic ST. Combined associations were assessed using adherence criteria (≥ 60 min/day of moderate‐intensity PA plus ≥ 3 days/week of vigorous‐intensity PA) and ST classification (≤ 2 h/day).
Results
In independent analysis, adolescents meeting PA guidelines consistently showed lower odds of obesity across all grades. For sedentary time, the ≤ 2 h/day group had the lowest odds, and a dose–response relationship was observed up to 6–8 h/day across grades. In combined analysis, adolescents meeting both PA and ST guidelines had the lowest obesity prevalence (OR = 0.54, 95% CI: 0.50–0.59).
Implications for School Health Policy, Practice, and Equity
Schools should promote daily PA and reduce ST. Since behavioral risk factors differ by developmental stage (e.g., academic stress in higher grades), grade‐tailored strategies are essential for effective obesity prevention.
Conclusions
Higher PA levels and lower ST are independently and jointly associated with lower obesity prevalence among Korean adolescents across all school grades.
Keywords: BMI, epidemiology, KYRBS, lifestyle behaviors, public health, youth health
1. Introduction
Adolescents with obesity are more likely to remain obese as adults, highlighting the importance of early prevention [1]. Adolescents with obesity are also at increased risk for health complications such as metabolic syndrome, diabetes, and cardiovascular disease [2, 3]. Over the past decade, the prevalence of obesity among Korean adolescents has nearly doubled, rising from 9.7% in 2012 to 19.3% in 2021 [4, 5]. This increase has been particularly pronounced in recent years, rising from 13.7% in 2019 to 19.3% in 2021, aligning with the COVID‐19 pandemic, which was characterized by significant lifestyle changes, including reduced opportunities for physical activity (PA) and increased sedentary behavior [6, 7].
PA is a key factor in obesity prevention and healthy weight management, contributing to improved metabolic function and body composition by reducing body fat and enhancing overall health [8, 9, 10]. In particular, moderate‐to‐vigorous PA plays a crucial role in weight control and obesity prevention by increasing energy expenditure, improving cardiovascular endurance, and enhancing insulin sensitivity [11]. Current PA guidelines recommend that adolescents should engage in at least 60 min of moderate intensity physical activity (MPA) daily including vigorous intensity physical activity (VPA) at least 3 days/week [12]. The adherence to PA guidelines among adolescents is remarkably low, with accelerometer‐based studies reporting adherence rates of less than 10% [13, 14, 15].
On the other hand, sedentary time (ST) spent on nonacademic activities, such as leisure‐related screen use and socializing while sitting, has emerged as an independent risk factor for obesity. This association can be explained by mechanisms such as energy imbalance where reduced energy expenditure is often coupled with increased intake and behavioral clustering, in which sedentary habits frequently co‐occur with other unhealthy behaviors like poor diet or irregular sleep patterns [16]. Since the 2000s, leisure‐time sedentary behavior, such as computer use, has been steadily increasing. This increase is positively associated with the development of obesity and is also considered a risk factor for cardiovascular diseases [17, 18].
Previous studies have extensively investigated the independent relationships between ST and obesity, as well as PA and obesity [19, 20]. The combined association of PA and ST with obesity in adolescents has been studied in only a small number of cases, but these studies were limited by relatively small sample sizes and by the use of broad categories for PA and ST rather than more detailed analyses [21, 22, 23, 24]. In addition, several prior studies defined obesity using age in years rather than exact age in months. However, since adolescence is a critical period of rapid somatic growth and physiological change, relying on yearly age intervals may overlook significant monthly variations in body composition. Utilizing exact age in months allows for a more precise calculation of body mass index (BMI) z‐scores, thereby reducing misclassification bias and facilitating more accurate developmental comparisons [25].
Previous research suggests that the relationship between physical activity, ST, and obesity is influenced by various confounding factors. Age and sex are fundamental to adolescent developmental stages [26]. Furthermore, household economic status is a key social determinant known to influence lifestyle choices and obesity risk [27], while academic performance has been linked to weight status in adolescents [28]. Environmental factors, such as residence area (urban–rural differences), can also impact lifestyle opportunities and obesity prevalence [29]. Additionally, psychological factors like perceived stress are associated with a higher risk of obesity, often mediated through behavioral alterations [30], and an adolescent's weight control effort is strongly linked to their health behaviors [31]. Substance use, including alcohol [32] and tobacco consumption [33], has also been positively related to obesity prevalence. Therefore, to isolate the independent and combined associations of PA and ST with obesity, it is essential to adjust for these potential covariates.
To address the methodological limitations of previous studies and account for these multifactorial influences, the present study conducted a more detailed analysis of PA levels and ST and further examined both their independent and combined associations with obesity prevalence among Korean adolescents across different school grades. By analyzing these relationships, the study aims to provide valuable insights into the behavioral factors associated with adolescent obesity.
2. Methods
2.1. Data Source
The Korea Youth Risk Behavior Web‐Based Survey (KYRBS) is a nationally representative cross‐sectional survey conducted annually by the Korea Disease Control and Prevention Agency (KDCA), the Ministry of Health and Welfare, and the Ministry of Education since 2005. The survey employs a stratified multistage cluster sampling design to recruit adolescents aged 12–18 years. In the first stage, approximately 800 schools (400 middle and 400 high schools) are selected annually based on region and school type. In the second stage, one class per grade is randomly sampled from each school. The reliability and validity of the KYRBS questionnaire have been verified in previous studies, confirming moderate‐to‐high kappa coefficients and concordance with objective measures [34]. In this study, we utilized comprehensive data spanning 11 years (2013–2023). While complex survey weights were not applied, we adjusted for key sociodemographic factors in the multivariate models to account for potential variations related to the sampling design.
2.2. Study Participants
Of the initial 672,211 participants, 18,712 were excluded due to missing height and weight data, 52 due to missing PA data, and 6 had missing covariate data. For ST, 38,498 participants were excluded due to missing data or values exceeding 960 min per day. This cutoff was applied to remove biologically implausible values exceeding the maximum plausible daily waking hours [35, 36]. After these exclusions, a final sample of 614,943 adolescents was included in the analysis.
In South Korea, the education system includes middle school (grades 1–3; equivalent to U.S. grades 7–9) and high school (grades 1–3; equivalent to U.S. grades 10–12).
2.3. Outcome
2.3.1. Obesity
BMI was calculated from self‐reported height and weight, as weight in kilograms divided by the square of height in meters (kg/m2). Obesity was defined according to the WHO growth reference, with children and adolescents classified as obese if their BMI‐for‐age (based on exact age in months) was greater than +2 standard deviations (z‐score). While BMI is a widely used proxy for adiposity, it does not differentiate between fat and muscle mass; thus, there is a potential for misclassification, particularly among athletic youth with high muscle mass. For comparison, obesity prevalence based on the 2017 Korean Child and Adolescent Growth Charts (≥ 95th percentile for age and sex, calculated using exact age in months) is presented in the Table S1 and Figure S1.
2.4. Exposures
2.4.1. Physical Activity Levels
The PA guidelines in this study followed the WHO standards [12]. MPA was defined by the number of days on which participants reported an elevated heart rate and slight shortness of breath. The relevant survey question asked: “Over the past 7 days, on how many days did you engage in PA that raised your heart rate above normal and made you feel mildly out of breath, and on average, how many minutes did you spend exercising each day?” VPA was defined by the number of days on which participants engaged in activity that made them breathe hard or sweat. The relevant survey question asked: “Over the past 7 days, on how many days did you perform VPA that made you breathe heavily or sweat, and on average, how many minutes did you spend exercising each day?” Meeting PA guidelines was defined as accumulating at least 60 min of MPA per day plus at least 3 days of VPA per week [6].
2.4.2. Nonacademic Sedentary Time
ST was categorized based on Canada's 24‐Hour Movement Guidelines, which suggest a 2 h daily limit for sedentary behavior outside of academic purposes [37]. Accordingly, participants with ST ≤ 2 h were defined as meeting the ST guidelines. Although specific quantitative guidelines for ST have not yet been established in Korea, these guidelines are widely accepted internationally and have been employed in previous studies involving Korean adolescents to ensure comparability [38]. In KYRBS, ST was calculated as the average daily ST, derived from the question: “Over the past 7 days, how much time per day on average did you spend in sedentary activities for nonacademic purposes.” The time spent was averaged as (weekday min × 5 + weekend min × 2)/7 with those reporting 2 h or less classified as meeting the guidelines and those above 2 h classified as not meeting the guidelines. To examine the detailed association between ST and obesity, participants were categorized into five groups using 2‐h intervals (from ≤ 2 h to > 8 h).
2.5. Covariates
The following covariates were selected for adjustment based on previous literature demonstrating their potential association with physical activity, ST, and obesity.
The analysis adjusted for sociodemographic factors including age (continuous), sex (male/female), and residence area (classified into metropolitan, city, and rural). Household economic status and academic performance were each categorized into three groups: low (combining “low” and “middle low”), middle, and high (combining “middle high” and “high”) based on self‐reported measures.
Psychosocial and behavioral covariates included perceived stress, which was re‐classified into three groups (“no/little stress,” “a little stress,” and “a great deal of stress”), and weight control effort, categorized into four groups (no effort, efforts to lose, gain, or maintain weight). Alcohol and tobacco consumption were included as binary variables (yes/no) based on lifetime experience.
2.6. Statistical Analysis
Descriptive statistics summarized participants' characteristics by grade, presented as mean ± standard deviation (SD) for continuous variables and n (%) for categorical variables. Multivariable logistic regression models estimated independent and combined associations of PA and ST with obesity prevalence, adjusting for all aforementioned covariates. Results are reported as odds ratios (OR) with 95% confidence intervals (CI).
To analyze the independent association, MPA and VPA frequencies were examined separately using 0 days/week as the reference value, with mutual adjustment for each other. ST was analyzed in 2‐h intervals using ≤ 2 h/day as the reference. For combined analyses, participants were categorized into four subgroups: “Neither” (reference), “Only PA,” “Only ST,” and “Both” (meeting both guidelines); all covariates were included in this analysis. To explore potential differences by sex, all primary analyses were repeated for male and female groups separately; detailed results are presented in Tables S2 and S3. All analyses were conducted using R software (version 4.4.1; R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was defined as p < 0.05.
3. Results
Descriptive statistics by grades are presented in Table 1. The mean age of the 614,943 adolescents was 15 ± 1.8 years and distribution of females was 49.2%. The proportion of adolescents reporting “a great deal of stress” increased with grade level (33.0% to 43.1%; Table 1). Furthermore, 48.3% reported no weight control efforts, peaking in 3rd‐grade high school students (53.4%), while efforts to lose weight were lowest in this group (30.4%). The overall prevalence of obesity among adolescents was 6.5%, and the prevalence was highest among 3rd‐grade high school students at 8.2%.
TABLE 1.
Descriptive statistics stratified by grades (n (%)).
| Total | Middle school | High school | |||||
|---|---|---|---|---|---|---|---|
| 1st | 2nd | 3rd | 1st | 2nd | 3rd | ||
| (N = 614,943) | (N = 104,465) | (N = 104,644) | (N = 105,573) | (N = 99,700) | (N = 99,045) | (N = 96,095) | |
| Age (mean ± SD) | 15 ± 1.8 | 12.6 ± 0.5 | 13.6 ± 0.5 | 14.6 ± 0.5 | 15.6 ± 0.5 | 16.5 ± 0.5 | 17.5 ± 0.5 |
| Sex (female, %) | 302,479 (49.2) | 50,950 (48.5) | 51,380 (48.8) | 52,265 (49.2) | 49,595 (49.3) | 49,489 (49.5) | 48,800 (50.0) |
| Household economic status | |||||||
| Low | 88,424 (14.4) | 9,354 (8.9) | 11,466 (10.9) | 13,610 (12.8) | 16,045 (15.9) | 18,367 (18.4) | 19,582 (20.1) |
| Middle | 292,749 (47.6) | 44,959 (42.8) | 49,000 (46.5) | 50,950 (47.9) | 49,918 (49.6) | 49,598 (49.6) | 48,324 (49.5) |
| High | 233,770 (38.0) | 50,758 (48.3) | 44,879 (42.6) | 41,767 (39.3) | 34,675 (34.5) | 32,059 (32.1) | 29,632 (30.4) |
| Academic performance | |||||||
| Low | 199,096 (32.4) | 24,384 (23.2) | 33,885 (32.2) | 34,177 (32.1) | 36,861 (36.6) | 37,014 (37.0) | 32,775 (33.6) |
| Middle | 180,405 (29.3) | 31,512 (30.0) | 28,837 (27.4) | 27,376 (25.7) | 30,163 (30.0) | 30,994 (31.0) | 31,523 (32.3) |
| High | 235,442 (38.3) | 49,175 (46.8) | 42,623 (40.5) | 44,774 (42.1) | 33,614 (33.4) | 32,016 (32.0) | 33,240 (34.1) |
| Residence area | |||||||
| Big city | 49,731 (8.1) | 8,323 (7.9) | 8,459 (8.0) | 8,631 (8.1) | 8,209 (8.2) | 8,136 (8.1) | 7,973 (8.2) |
| Middle – small city | 272,065 (44.2) | 45,455 (43.3) | 45,945 (43.6) | 46,637 (43.9) | 45,190 (44.9) | 4,491 (44.9) | 4,392 (45.0) |
| Very small | 293,147 (47.7) | 51,293 (48.8) | 50,941 (48.4) | 51,059 (48.0) | 47,239 (46.9) | 4,697 (47.0) | 4,564 (46.8) |
| Smoking consumption | 81,965 (13.3) | 3,832 (3.6) | 8,220 (7.8) | 12,877 (12.1) | 16,419 (16.3) | 19,532 (19.5) | 21,085 (21.6) |
| Alcohol consumption | 230,984 (37.6) | 20,630 (19.7) | 26,582 (25.2) | 33,729 (31.7) | 40,792 (40.5) | 51,509 (51.5) | 57,650 (59.1) |
| Stress level | |||||||
| No stress | 118,498 (19.3) | 26,042 (24.8) | 22,635 (21.5) | 21,417 (20.1) | 17,765 (17.7) | 16,049 (16.0) | 14,590 (15.0) |
| A little stress | 264,859 (43.1) | 44,355 (42.2) | 45,561 (43.2) | 46,377 (43.6) | 44,597 (44.3) | 43,086 (43.1) | 40,883 (41.9) |
| A great deal of stress | 231,586 (37.7) | 34,674 (33.0) | 37,149 (35.3) | 38,533 (36.2) | 38,276 (38.0) | 40,889 (40.9) | 42,065 (43.1) |
| Weight control effort | |||||||
| No effort | 296,905 (48.3) | 48,250 (45.9) | 49,032 (46.5) | 49,649 (46.7) | 49,834 (49.5) | 48,034 (48.0) | 52,106 (53.4) |
| To reduce | 203,953 (33.2) | 34,133 (32.5) | 35,441 (33.6) | 36,602 (34.4) | 33,296 (33.1) | 34,826 (34.8) | 29,655 (30.4) |
| To increase | 42,054 (6.8) | 6,864 (6.5) | 7,154 (6.8) | 7,656 (7.2) | 7,180 (7.1) | 7,159 (7.2) | 6,041 (6.2) |
| To maintain | 72,031 (11.7) | 15,824 (15.1) | 13,718 (13.0) | 12,420 (11.7) | 10,328 (10.3) | 10,005 (10.0) | 9,736 (10.0) |
| Body mass index (BMI) (mean ± SD) | 21.2 ± 3.5 | 20.2 ± 3.4 | 20.6 ± 3.4 | 21.0 ± 3.4 | 21.5 ± 3.4 | 21.8 ± 3.4 | 22.1 ± 3.4 |
| BMI status | |||||||
| Normal | 574,871 (93.5) | 99,789 (95.0) | 99,540 (94.5) | 99,968 (94.0) | 93,557 (93.0) | 92,484 (92.5) | 89,533 (91.8) |
| Obesity male | 26,498 (4.3) | 3,605 (3.4) | 4,024 (3.8) | 4,411 (4.1) | 4,693 (4.7) | 4,796 (4.8) | 4,969 (5.1) |
| Obesity female | 13,574 (2.2) | 1,677 (1.6) | 1,781 (1.7) | 1,948 (1.8) | 2,388 (2.4) | 2,744 (2.7) | 3,036 (3.1) |
Note: Data are presented as n (%) for categorical variables and mean ± standard deviation for continuous variables.
Abbreviations: BMI, body mass index; SD, standard deviation.
The distribution of PA levels, PA guidelines adherence, and ST categories is detailed in Table 2. PA levels exhibited a distinct downward trend with increasing grade levels. The frequency of engaging in both moderate and vigorous PA progressively declined from middle school to high school, resulting in the lowest adherence to PA guidelines among 3rd‐grade high school students. Conversely, ST patterns did not follow this negative trend; interestingly, the proportion of students meeting the ST guideline (≤ 2 h/day) was highest among 3rd‐grade high school students, while the prevalence of excessive ST (> 8 h/day) remained relatively low in high school grades.
TABLE 2.
Descriptive statistics of PA levels, PA guidelines, and ST categories stratified by grades (n (%)).
| Total | Middle school | High school | |||||
|---|---|---|---|---|---|---|---|
| 1st | 2nd | 3rd | 1st | 2nd | 3rd | ||
| Moderate physical activity (MPA) weekly frequency (mean ± SD) | 2.0 ± 2.1 | 2.2 ± 2.2 | 2.2 ± 2.2 | 2.2 ± 2.2 | 1.8 ± 2.0 | 1.8 ± 2.0 | 1.6 ± 1.9 |
| 0 days | 214,636 (34.9) | 33,940 (32.3) | 33,204 (31.5) | 34,054 (32.0) | 36,352 (36.1) | 36,616 (36.6) | 40,470 (41.5) |
| 1 day | 97,653 (15.9) | 15,221 (14.5) | 15,304 (14.5) | 15,635 (14.7) | 17,113 (17.0) | 17,059 (17.1) | 17,321 (17.8) |
| 2 days | 94,134 (15.3) | 14,311 (13.6) | 14,871 (14.1) | 15,006 (14.1) | 17,733 (17.6) | 17,194 (17.2) | 15,019 (15.4) |
| 3 days | 78,115 (12.7) | 14,536 (13.8) | 15,329 (14.6) | 15,087 (14.2) | 12,030 (12.0) | 11,554 (11.6) | 9,579 (9.8) |
| 4 days | 40,952 (6.7) | 8,519 (8.1) | 8,543 (8.1) | 8,165 (7.7) | 5,717 (5.7) | 5,433 (5.4) | 4,575 (4.7) |
| 5 days | 39,706 (6.5) | 8,367 (8.0) | 8,061 (7.7) | 8,036 (7.6) | 5,175 (5.1) | 5,359 (5.4) | 4,708 (4.8) |
| 6 days | 14,178 (2.3) | 2,949 (2.8) | 2,799 (2.7) | 2,834 (2.7) | 1,833 (1.8) | 1,996 (2.0) | 1,767 (1.8) |
| 7 days | 35,569 (5.8) | 7,228 (6.9) | 7,234 (6.9) | 7,510 (7.1) | 4,685 (4.7) | 4,813 (4.8) | 4,099 (4.2) |
| Vigorous physical activity (VPA) weekly frequency (mean ± SD) | 2.0 ± 1.7 | 2.4 ± 1.8 | 2.3 ± 1.8 | 2.2 ± 1.8 | 1.8 ± 1.7 | 1.8 ± 1.7 | 1.6 ± 1.7 |
| 0 days | 165,118 (26.9) | 20,212 (19.2) | 23,130 (22.0) | 25,723 (24.2) | 29,401 (29.2) | 31,017 (31.0) | 35,635 (36.5) |
| 1 day | 115,087 (18.7) | 18,866 (18.0) | 18,424 (17.5) | 18,485 (17.4) | 19,926 (19.8) | 19,628 (19.6) | 19,758 (20.3) |
| 2 days | 111,792 (18.2) | 18,812 (17.9) | 18,056 (17.1) | 17,877 (16.8) | 20,985 (20.9) | 19,647 (19.6) | 16,415 (16.8) |
| 3 days | 87,144 (14.2) | 17,857 (17.0) | 18,044 (17.1) | 16,987 (16.0) | 12,509 (12.4) | 11,817 (11.8) | 9,930 (10.2) |
| 4 days | 42,670 (6.9) | 9,178 (8.7) | 8,900 (8.4) | 8,533 (8.0) | 5,604 (5.6) | 5,614 (5.6) | 4,841 (5.0) |
| 5 or more days | 93,132 (15.1) | 20,146 (19.2) | 18,791 (17.8) | 18,722 (17.6) | 12,213 (12.1) | 12,301 (12.3) | 10,959 (11.2) |
| Adherence to physical activity (PA) guidelines | 33,051 (5.4) | 6,725 (6.4) | 6,747 (6.4) | 7,072 (6.7) | 4,332 (4.3) | 4,429 (4.4) | 3,746 (3.8) |
| Sedentary time (ST) categories (mean ± SD) | 204.5 ± 137.5 | 204.3 ± 142.4 | 216.5 ± 142.3 | 218.2 ± 140.7 | 195.9 ± 131.8 | 199.3 ± 131.5 | 191.1 ± 132.6 |
| ≤ 2 h | 190,639 (31.0) | 34,015 (32.4) | 28,957 (27.5) | 28,200 (26.5) | 33,099 (32.9) | 31,414 (31.4) | 34,954 (35.8) |
| 2 <, ≤ 4 h | 243,889 (39.7) | 39,714 (37.8) | 41,811 (39.7) | 42,712 (40.2) | 40,608 (40.4) | 41,093 (41.1) | 37,951 (38.9) |
| 4 <, ≤ 6 h | 110,867 (18.0) | 18,591 (17.7) | 20,570 (19.5) | 21,562 (20.3) | 17,126 (17.0) | 17,602 (17.6) | 15,416 (15.8) |
| 6 <, ≤ 8 h | 40,638 (6.6) | 7,271 (6.9) | 8,202 (7.8) | 8,063 (7.6) | 5,791 (5.8) | 5,909 (5.9) | 5,402 (5.5) |
| > 8 h | 28,910 (4.7) | 5,480 (5.2) | 5,805 (5.5) | 5,790 (5.4) | 4,014 (4.0) | 4,006 (4.0) | 3,815 (3.9) |
Note: Data are presented as n (%) for categorical variables and mean ± SD for continuous variables. PA guidelines adherence is defined as engaging in MPA 7 days/week and VPA ≥ 3 days/week; ST categories represent average daily nonacademic sedentary time, classified into 2 h intervals.
Abbreviations: MPA, moderate physical activity; SD, standard deviation; ST, sedentary time; VPA, vigorous physical activity.
The results of the multivariate logistic regression analysis on the independent association of PA levels and ST categories with obesity are presented in Table 3. In the total population analysis, compared to adolescents who did not engage in any MPA per week, those who performed at least 60 min of MPA daily exhibited a lower prevalence of obesity (OR = 0.90, 95% CI: 0.85–0.95). However, grade‐specific analyses revealed variations, with statistical significance observed only in 3rd‐grade middle school and 3rd‐grade high school students. Similarly, participating in VPA for 5 or more days was associated with decreased obesity in the total population (OR = 0.86, 95% CI: 0.83–0.90), yet this association was not significant in 3rd‐grade middle school students. These grade‐level inconsistencies suggest that single‐intensity measures may be sensitive to developmental or behavioral differences across grades. In contrast, adherence to PA guidelines, which integrates both MPA and VPA, showed a consistent and significantly lower prevalence of obesity across all grade levels compared to those not meeting the guidelines (Total OR = 0.73, 95% CI: 0.70–0.77). This indicates that a comprehensive measure of PA provides a more robust predictor of obesity than single intensity metrics alone.
TABLE 3.
Independent association between physical activity, sedentary time, and obesity stratified by grades.
| Total | Middle school | High school | |||||
|---|---|---|---|---|---|---|---|
| 1st | 2nd | 3rd | 1st | 2nd | 3rd | ||
| Moderate physical activity (MPA) weekly frequency | |||||||
| 0 day/week | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) |
| 1 day/week | 1.04 (1.01–1.08)** | 0.99 (0.91–1.09) | 1.00 (0.92–1.10) | 1.02 (0.94–1.11) | 1.08 (1.00–1.16)* | 1.13 (1.05–1.21)* | 1.03 (0.96–1.11) |
| 2 days/week | 1.08 (1.05–1.12)*** | 1.09 (0.99–1.20) | 1.02 (0.94–1.12) | 1.07 (0.98–1.17) | 1.10 (1.02–1.19)** | 1.12 (1.04–1.21)** | 1.11 (1.03–1.19)* |
| 3 days/week | 1.14 (1.10–1.18)*** | 1.09 (0.99–1.19) | 1.08 (0.98–1.18) | 1.13 (1.04–1.24)** | 1.19 (1.09–1.29)*** | 1.27 (1.17–1.39)*** | 1.12 (1.02–1.23)* |
| 4 days/week | 1.08 (1.03–1.13)** | 1.10 (0.98–1.23) | 1.11 (1.00–1.24)* | 0.99 (0.88–1.10) | 1.20 (1.07–1.34)*** | 1.11 (0.98–1.25) | 1.05 (0.93–1.19) |
| 5 days/week | 1.15 (1.09–1.20)*** | 1.21 (1.08–1.36)*** | 1.07 (0.96–1.20) | 1.21 (1.08–1.35)*** | 1.21 (1.07–1.37)*** | 1.15 (1.01–1.30) | 1.03 (0.91–1.17) |
| 6 days/week | 0.99 (0.92–1.06) | 0.96 (0.80–1.16) | 0.94 (0.78–1.13) | 0.89 (0.74–1.06) | 1.20 (1.00–1.45)* | 1.14 (0.95–1.37) | 0.88 (0.72–1.08) |
| 7 days/week | 0.90 (0.85–0.95)*** | 0.88 (0.77–1.01) | 0.89 (0.78–1.02) | 0.87 (0.76–0.98)* | 1.01 (0.87–1.16) | 0.89 (0.77–1.03) | 0.86 (0.74–1.00)* |
| Vigorous physical activity (VPA) weekly frequency | |||||||
| 0 day/week | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) |
| 1 day/week | 1.02 (0.99–1.06) | 1.04 (0.94–1.14) | 1.04 (0.95–1.14) | 1.13 (1.04–1.23)** | 0.97 (0.90–1.05) | 0.98 (0.91–1.06) | 1.03 (0.96–1.11) |
| 2 days/week | 1.03 (0.99–1.06) | 0.98 (0.89–1.08) | 1.04 (0.95–1.15) | 1.04 (0.95–1.14) | 1.04 (0.96–1.12)* | 1.05 (0.97–1.13) | 1.03 (0.96–1.12) |
| 3 days/week | 0.98 (0.95–1.02) | 0.96 (0.87–1.06) | 1.06 (0.96–1.16) | 1.07 (0.98–1.18) | 0.97 (0.88–1.06) | 0.97 (0.88–1.06) | 0.92 (0.84–1.02) |
| 4 days/week | 0.92 (0.87–0.96)*** | 0.85 (0.75–0.97)* | 1.00 (0.89–1.13) | 1.03 (0.92–1.15) | 0.89 (0.79–1.01) | 0.90 (0.79–1.01) | 0.88 (0.77–0.99)* |
| 5 or more days/week | 0.86 (0.83–0.90) *** | 0.83 (0.75–0.93)** | 0.87 (0.79–0.97)* | 0.92 (0.83–1.02) | 0.81 (0.73–0.91)** | 0.88 (0.79–0.99)* | 0.86 (0.77–0.96)* |
| Adherence to physical activity (PA) guidelines | |||||||
| Not meeting | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) |
| Meeting | 0.73 (0.70–0.77)*** | 0.75 (0.67–0.85)*** | 0.73 (0.65–0.81)*** | 0.73 (0.65–0.81)*** | 0.78 (0.69–0.88)*** | 0.71 (0.63–0.81)*** | 0.70 (0.62–0.80)*** |
| Sedentary time (ST) categories | |||||||
| ≤ 2 h/day | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) |
| 2–4 h/day | 1.21 (1.18–1.24)*** | 1.30 (1.21–1.40)*** | 1.13 (1.05–1.21)** | 1.21 (1.13–1.29)*** | 1.14 (1.07–1.21)*** | 1.27 (1.20–1.35)*** | 1.24 (1.17–1.31)*** |
| 4–6 h/day | 1.44 (1.39–1.48)*** | 1.63 (1.50–1.77)*** | 1.45 (1.34–1.57)*** | 1.33 (1.23–1.44)*** | 1.29 (1.20–1.39)*** | 1.50 (1.40–1.61)*** | 1.49 (1.39–1.60)*** |
| 6–8 h/day | 1.63 (1.56–1.70)*** | 1.90 (1.71–2.11)*** | 1.61 (1.46–1.78)*** | 1.51 (1.37–1.67)*** | 1.53 (1.39–1.69)*** | 1.64 (1.49–1.81)*** | 1.66 (1.51–1.83)*** |
| > 8 h/day | 1.69 (1.61–1.77)*** | 2.07 (1.84–2.32)*** | 1.70 (1.52–1.90)*** | 1.41 (1.26–1.58)*** | 1.63 (1.46–1.83)*** | 1.57 (1.40–1.77)*** | 1.84 (1.66–2.04)*** |
Note: Data are presented as odds ratio (95% confidence interval).PA guidelines adherence is defined as engaging in MPA 7 days/week and VPA ≥ 3 days/week; ST categories represent average daily nonacademic sedentary time, classified into 2 h intervals. Adjusted for sex, age, household economic status, academic performance, residence area, perceived stress level, weight control effort, and the experience of alcohol and tobacco.
Abbreviations: MPA, moderate physical activity; ST, sedentary time; VPA, vigorous physical activity.
p < 0.05.
p < 0.01.
p < 0.001.
In the total population, the association between ST and obesity prevalence showed a dose–response trend across ST categories. Using the group with ST ≤ 2 h as the reference, the odds of obesity increased progressively across categories, reaching the highest odds in the > 8 h/day group (OR = 1.69, 95% CI: 1.61–1.77). This increasing trend was generally consistent across most grades. However, in certain grades (e.g., 3rd‐grade middle school and 2nd‐grade high school), the odds ratios in the > 8 h/day group were slightly lower than those in the 6–8 h/day group. This plateau or slight attenuation observed at the extreme end of ST (> 8 h/day) may reflect reporting bias, as the accuracy of self‐reported duration tends to decrease for such extreme values.
The combined associations of adherence to PA and ST guidelines with obesity are presented in Figure 1. Notably, adolescents meeting both PA and ST guidelines had the lowest prevalence of obesity among all groups (OR = 0.54, 95% CI: 0.50–0.59), indicating an approximate 46% reduction in the odds of obesity compared to those meeting neither guideline. Adolescents adhering only to PA had a significantly lower prevalence of obesity (OR = 0.73, 95% CI: 0.69–0.78), and those adhering only to ST also had a significantly lower prevalence (OR = 0.75, 95% CI: 0.73–0.77), though the magnitude of reduction was smaller than in the combined group. A similar pattern was observed when obesity was defined using the 2017 Korean Child and Adolescent Growth Charts (see Figure S1). This trend was consistent across all grades, with both groups showing the lowest odds of obesity in each grade. Additional sex‐stratified analyses revealed some differences between males and females, but meeting both guidelines was consistently associated with the lowest odds of obesity across sexes (see Tables S2 and S3).
FIGURE 1.
Odds ratio by combinations of meeting PA guidelines and ST guidelines. PA, moderate‐to‐vigorous physical activity; ST, sedentary time; Adjusted for sex, age, household economic status, academic performance, residence area, perceived stress level, weight control effort, and the use of alcohol and tobacco.
4. Discussion
This study analyzed the distribution of MPA and VPA, as well as ST, by school grade among Korean adolescents and examined their independent and combined associations with the prevalence of obesity. Among the total sample, only 5.4% of students met the PA guidelines, and 31.0% met the ST guidelines, indicating that adherence to key health behavior recommendations related to obesity is generally low. In addition, PA levels tended to decrease with increasing grade level, a pattern that has been observed globally [39]. This decline in PA during adolescence has been well‐documented in international studies and is considered a critical factor contributing to the increased risk of obesity among older adolescents [40].
In the independent analysis of PA, engaging in daily MPA or in VPA at least 5 days per week was significantly associated with lower obesity prevalence; however, this association was not consistently significant across all grades. This may be explained by the fact that MPA and VPA were analyzed independently with mutual adjustment, suggesting that the association with lower obesity is weaker when only one type of PA is performed compared to both. In fact, adolescents who met the PA guidelines showed a significantly lower prevalence of obesity across all grades, supporting the notion that a combined and comprehensive PA pattern has a stronger association with a lower prevalence of obesity among adolescents.
In contrast, when ST was categorized in 2‐h intervals, obesity prevalence significantly increased with longer ST across all grade levels. This finding aligns with prior studies that have consistently reported positive associations between sedentary behaviors and the risk of obesity in adolescents [16]. These results highlight ST as a critical behavioral factor that may be relevant in understanding and addressing adolescent obesity.
In the combined analysis of PA and ST, students who met either guideline showed significantly lower obesity prevalence in all grades, with the lowest prevalence observed in those who met both. Notably, adolescents in the “only PA” or “only ST” groups also demonstrated lower obesity prevalence, suggesting that adherence to even one guideline may attenuate obesity likelihood, although the strongest association was evident when both behaviors were achieved together. This pattern is consistent with previous studies reporting that adolescents with higher PA and lower ST generally exhibit lower obesity prevalence [21, 22]. Overall, the combination of high PA and low ST was associated with the most favorable outcomes, underscoring the value of an integrated approach. In addition, sex‐stratified analyses showed that these associations were consistent across both males and females, supporting the robustness of the findings and reinforcing the need for universal health promotion strategies.
Contrary to previous findings, our study observed higher obesity prevalence among high school students, despite their lower ST compared to the overall average [41, 42]. Notably, 3rd‐grade high school students showed relatively high adherence to the ST guidelines, yet had the highest obesity prevalence—an apparently paradoxical result. Two possible explanations may account for this finding. First, analysis of KYRBS data revealed that as grade level increased, adolescents reported lower motivation to control their weight and higher levels of perceived stress. These psychosocial factors may have negatively affected their willingness to engage in health‐promoting behaviors, such as PA, even if ST decreased [43, 44]. In fact, among high school students in this study, low PA levels and reduced weight control efforts were observed alongside shorter ST durations. Second, PA and ST should be understood as independent behavioral domains. Prior studies have shown that individuals with similar amounts of ST can experience different health outcomes depending on their level of PA [45]. Similarly, in this study, while both PA and ST declined with increasing grade level, the reduction in ST did not correspond with an increase in PA or total energy expenditure. In other words, the decrease in ST may not reflect a positive health behavior change, but rather a broader reduction in overall activity levels.
4.1. Implications for School Health Policy, Practice, and Equity
Our grade‐stratified findings suggest actionable strategies for schools. First, given that obesity peaks in upper grades alongside lower weight control efforts and higher stress, reducing ST alone is insufficient. High schools should prioritize time‐efficient, curriculum‐embedded PA opportunities (e.g., brief activity breaks, protected MVPA blocks).
Second, meeting PA guidelines and limiting nonacademic ST were each independently associated with lower odds of obesity. However, addressing the limitation of prior research that focused mainly on independent effects, our joint analysis highlights that meeting both guidelines showed the lowest odds across grades. This synergistic protective effect can be explained by both biological and behavioral mechanisms. Biologically, PA increases energy expenditure and improves metabolic profiles, such as insulin sensitivity and lipid oxidation [46]. Conversely, excessive sedentary behavior is characterized by low energy expenditure and may physiologically impair lipoprotein lipase activity, which is crucial for lipid metabolism [47]. Behaviorally, the “displacement hypothesis” suggests that time spent in PA naturally displaces time available for sedentary behaviors [48]. Furthermore, screen‐based ST is often accompanied by mindless snacking; therefore, reducing ST may simultaneously decrease caloric intake, amplifying the benefits of PA [49]. Consequently, school programs should pair PA promotion with concrete ST management (e.g., setting ≤ 2 h/day nonacademic ST targets, designing “screen‐off” intervals linked to active opportunities). Messaging should emphasize this synergy: replacing ST with activity rather than assuming that less screen time automatically increases PA.
Third, the dose–response pattern for ST (clarified by our granular analysis of 2‐h intervals rather than the broad categories used in previous studies) supports grade‐specific counseling and schoolwide routines that curb prolonged sedentary blocks (e.g., movement prompts each class period), while acknowledging that some grades showed slightly lower ORs beyond 8 h/day. Finally, sex‐stratified analyses showed the core “both is best” association in males and females, underscoring an equity lens in program design: offer diverse, student‐preferred activities (competitive and noncompetitive), safe and affordable access, and supportive communication that attends to differing motivations and barriers. Given the very low PA‐guideline adherence (5.4%) and modest adherence to the ≤ 2 h/day sedentary criterion (31.0%), schools are a logical platform to close behavior gaps by grade. Routine monitoring of PA and nonacademic ST and targeted supports for students reporting high stress or low weight control motivation may help align school practice with developmental needs.
This study utilizes a large, nationally representative dataset (n = 614,943) spanning 11 years. A key strength is the use of exact age in months, which minimizes the misclassification bias often found in year‐based analyses. Additionally, the granular categorization of ST in 2‐h intervals and consistent findings across supplementary analyses reinforce the robustness of our results.
Nevertheless, several limitations should be noted. First, self‐reported data are subject to recall and social desirability biases; consequently, PA may have been overreported and ST underreported compared to objective measures. Second, the cross‐sectional design precludes causal inferences. Third, variables such as diet and sleep were unavailable or excluded due to data limitations. Furthermore, regarding specific types of sedentary behavior, this study focused exclusively on nonacademic ST to align with the Canadian 24‐Hour Movement Guidelines; thus, academic ST was not included. Fourth, BMI‐based definitions may misclassify muscle mass. Finally, data from 2013 to 2023 were pooled to maximize statistical power. While enabling robust stratified analyses, this aggregated approach may not capture specific temporal trends or period effects (e.g., COVID‐19) over the 11‐year period.
Even with these limitations, this study offers important evidence on the links between PA, ST, and obesity in a large, nationally representative sample of adolescents. The extra analyses also support the main findings: results were similar for boys and girls (Tables S2 and S3), and also when obesity was defined using the 2017 Korean Growth Charts (Table S1 and Figure 2). These checks reduce concern about measurement and classification and make the findings more reliable. Future studies should use objective tools (e.g., accelerometers) and follow adolescents over time to better understand cause‐and‐effect. Adding information on diet, sleep, and different sedentary activities would also give a fuller picture of these relationships.
FIGURE 2.
Flow chart of participants included for analysis.
5. Conclusion
In conclusion, our findings indicate that adhering to both PA and ST guidelines is associated with the lowest odds of obesity across all grades. Crucially, the distinct patterns observed underscore the developmental necessity of grade‐targeted interventions. Future policies should move beyond a “one‐size‐fits‐all” strategy to align support with the specific academic and biological needs of students at each developmental stage.
Ethics Statement
The analysis was based on publicly available, de‐identified data. The study procedures were approved by the KDCA, and the study was deemed exempt from institutional review board (IRB) review by Seoul National University (IRB No. E2505/003–002).
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Figure S1: Odds ratio for obesity (def ined using the 2017 Korean Child and Adolescent Growth Charts) by combinations of meeting PA guidelines and ST guidelines.
Table S1: Independent association between physical activity, sedentary time, and obesity (defined using the 2017 Korean Child and Adolescent Growth Charts), stratified by grades.
Table S2: Independent association between physical activity, sedentary time, and obesity stratified by sex.
Table S3: Combined association between adherence of physical activity guidelines and sedentary time guidelines with obesity stratified by sex.
Acknowledgments
The authors gratefully thank the KDCA for providing access to the KYRBS data. This research received no specific grant from any funding agency in the public, commercial, or not‐for‐profit sectors.
Data Availability Statement
The data that support the findings of this study are available in the Korea Disease Control and Prevention Agency at https://www.kdca.go.kr/. These data were derived from the following resources available in the public domain: Korea Youth Risk Behavior Web‐based Survey, https://www.kdca.go.kr/yhs/yhs/main.do.
References
- 1. Must A., “Does Overweight in Childhood Have an Impact on Adult Health?,” Nutrition Reviews 61, no. 4 (2003): 139–142, 10.1301/nr.2003.apr.139-142. [DOI] [PubMed] [Google Scholar]
- 2. Daniels S. R., Arnett D. K., Eckel R. H., et al., “Overweight in Children and Adolescents: Pathophysiology, Consequences, Prevention, and Treatment,” Circulation 111, no. 15 (2005): 1999–2012, 10.1161/01.CIR.0000161369.71722.10. [DOI] [PubMed] [Google Scholar]
- 3. Weiss R., Dziura J., Burgert T. S., et al., “Obesity and the Metabolic Syndrome in Children and Adolescents,” New England Journal of Medicine 350, no. 23 (2004): 2362–2374, 10.1056/NEJMoa031049. [DOI] [PubMed] [Google Scholar]
- 4. Obesity KSftSo , “Fact Sheet,” 2023, https://general.kosso.or.kr/html/.
- 5. Park H. K., Seo J. Y., Jung H. W., and Lim J. S., “Prevalence and Trends in Obesity and Severe Obesity in Korean Children and Adolescents, 2007‐2020: A Population‐Based Study,” Pediatrics International 65, no. 1 (2023): e15472, 10.1111/ped.15472. [DOI] [PubMed] [Google Scholar]
- 6. Kwon R., Koo M. J., Lee S. W., et al., “National Trends in Physical Activity Among Adolescents in South Korea Before and During the COVID‐19 Pandemic, 2009‐2021,” Journal of Medical Virology 95, no. 2 (2023): e28456, 10.1002/jmv.28456. [DOI] [PubMed] [Google Scholar]
- 7. Woo S., Yang H., Kim Y., Lim H., Song H. J., and Park K. H., “Sedentary Time and Fast‐Food Consumption Associated With Weight Gain During COVID‐19 Lockdown in Children and Adolescents With Overweight or Obesity,” Journal of Korean Medical Science 37, no. 12 (2022): e103, 10.3346/jkms.2022.37.e103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Janssen I. and LeBlanc A. G., “Systematic Review of the Health Benefits of Physical Activity and Fitness in School‐Aged Children and Youth,” International Journal of Behavioral Nutrition and Physical Activity 7, no. 1 (2010): 40, 10.1186/1479-5868-7-40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Carrel A. L., Clark R. R., Peterson S. E., Nemeth B. A., Sullivan J., and Allen D. B., “Improvement of Fitness, Body Composition, and Insulin Sensitivity in Overweight Children in a School‐Based Exercise Program: A Randomized, Controlled Study,” Archives of Pediatrics & Adolescent Medicine 159, no. 10 (2005): 963–968, 10.1001/archpedi.159.10.963. [DOI] [PubMed] [Google Scholar]
- 10. Sigal R. J., Alberga A. S., Goldfield G. S., et al., “Effects of Aerobic Training, Resistance Training, or Both on Percentage Body Fat and Cardiometabolic Risk Markers in Obese Adolescents: The Healthy Eating Aerobic and Resistance Training in Youth Randomized Clinical Trial,” JAMA Pediatrics 168, no. 11 (2014): 1006–1014, 10.1001/jamapediatrics.2014.1392. [DOI] [PubMed] [Google Scholar]
- 11. Lobelo F., Muth N. D., Hanson S., et al., “Physical Activity Assessment and Counseling in Pediatric Clinical Settings,” Pediatrics 145, no. 3 (2020): e20193992, 10.1542/peds.2019-3992. [DOI] [PubMed] [Google Scholar]
- 12. Organization WH , WHO Guidelines on Physical Activity and Sedentary Behaviour (World Health Organization (WHO), 2020). [PubMed] [Google Scholar]
- 13. Jung‐Hyun Kim I.‐K. J., “Relationship Between Practice of Physical Activities Guideline and Health‐Related Factors in Adolescents: Based on 11th (2015) Korea Youth Risk Behavior Web‐Based Survey(KYRBS),” Journal of Korean Association of Physical Education and Sport for Girls and Women 30, no. 4 (2016): 335–355. [Google Scholar]
- 14. Troiano R. P., Berrigan D., Dodd K. W., Masse L. C., Tilert T., and McDowell M., “Physical Activity in the United States Measured by Accelerometer,” Medicine and Science in Sports and Exercise 40, no. 1 (2008): 181–188, 10.1249/mss.0b013e31815a51b3. [DOI] [PubMed] [Google Scholar]
- 15. Aubert S., Barnes J. D., Demchenko I., et al., “Global Matrix 4.0 Physical Activity Report Card Grades for Children and Adolescents: Results and Analyses From 57 Countries,” Journal of Physical Activity & Health 19, no. 11 (2022): 700–728, 10.1123/jpah.2022-0456. [DOI] [PubMed] [Google Scholar]
- 16. Fang K., Mu M., Liu K., and He Y., “Screen Time and Childhood Overweight/Obesity: A Systematic Review and Meta‐Analysis,” Child: Care, Health and Development 45, no. 5 (2019): 744–753, 10.1111/cch.12701. [DOI] [PubMed] [Google Scholar]
- 17. Yang L., Cao C., Kantor E. D., et al., “Trends in Sedentary Behavior Among the US Population, 2001–2016,” JAMA 321, no. 16 (2019): 1587–1597, 10.1001/jama.2019.3636. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Felez‐Nobrega M., Raine L. B., Haro J. M., Wijndaele K., and Koyanagi A., “Temporal Trends in Leisure‐Time Sedentary Behavior Among Adolescents Aged 12‐15 Years From 26 Countries in Asia, Africa, and the Americas,” International Journal of Behavioral Nutrition and Physical Activity 17, no. 1 (2020): 102, 10.1186/s12966-020-01010-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Bhuiyan M. U., Zaman S., and Ahmed T., “Risk Factors Associated With Overweight and Obesity Among Urban School Children and Adolescents in Bangladesh: A Case‐Control Study,” BMC Pediatrics 13 (2013): 72, 10.1186/1471-2431-13-72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Godakanda I., Abeysena C., and Lokubalasooriya A., “Sedentary Behavior During Leisure Time, Physical Activity and Dietary Habits as Risk Factors of Overweight Among School Children Aged 14–15 Years: Case Control Study,” BMC Research Notes 11, no. 1 (2018): 186, 10.1186/s13104-018-3292-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. De Bourdeaudhuij I., Verloigne M., Maes L., et al., “Associations of Physical Activity and Sedentary Time With Weight and Weight Status Among 10‐ to 12‐Year‐Old Boys and Girls in Europe: A Cluster Analysis Within the ENERGY Project,” Pediatric Obesity 8, no. 5 (2013): 367–375, 10.1111/j.2047-6310.2012.00117.x. [DOI] [PubMed] [Google Scholar]
- 22. Crowe M., Sampasa‐Kanyinga H., Saunders T. J., Hamilton H. A., Benchimol E. I., and Chaput J. P., “Combinations of Physical Activity and Screen Time Recommendations and Their Association With Overweight/Obesity in Adolescents,” Canadian Journal of Public Health 111, no. 4 (2020): 515–522, 10.17269/s41997-020-00313-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Lee S.‐H. and Lee J.‐H., “Analysis on the Relationship Between Eating Behavior, Physical Activities and Youth Obesity: Based on the Korea Youth Risk Behavior Survey for High School Students in 2016˜2018,” Information 11, no. 3 (2020): 169, 10.3390/info11030169. [DOI] [Google Scholar]
- 24. Cho K. O., Lee S., and Kim Y. S., “Physical Activity and Sedentary Behavior Are Independently Associated With Weight in Korean Adolescents,” Journal of Lifestyle Medicine 4, no. 1 (2014): 47–54, 10.15280/jlm.2014.4.1.47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Racette S. B., Yu L., DuPont N. C., and Clark B. R., “BMI‐For‐Age Graphs With Severe Obesity Percentile Curves: Tools for Plotting Cross‐Sectional and Longitudinal Youth BMI Data,” BMC Pediatrics 17, no. 1 (2017): 130, 10.1186/s12887-017-0885-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Choi J., Yoon T. W., Yu M. H., Kang D. R., and Choi S., “Gender and Age Differences in the Prevalence and Associated Factors of Metabolic Syndrome Among Children and Adolescents in South Korea,” Child Health Nursing Research 27, no. 2 (2021): 160–170, 10.4094/chnr.2021.27.2.160. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Goodman E., Slap G. B., and Huang B., “The Public Health Impact of Socioeconomic Status on Adolescent Depression and Obesity,” American Journal of Public Health 93, no. 11 (2003): 1844–1850, 10.2105/ajph.93.11.1844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Crosnoe R. and Muller C., “Body Mass Index, Academic Achievement, and School Context: Examining the Educational Experiences of Adolescents at Risk of Obesity,” Journal of Health and Social Behavior 45, no. 4 (2004): 393–407, 10.1177/002214650404500403. [DOI] [PubMed] [Google Scholar]
- 29. Johnson J. A. and Johnson A. M., “Urban‐Rural Differences in Childhood and Adolescent Obesity in the United States: A Systematic Review and Meta‐Analysis,” Childhood Obesity 11, no. 3 (2015): 233–241, 10.1089/chi.2014.0085. [DOI] [PubMed] [Google Scholar]
- 30. Kappes C., Stein R., Korner A., Merkenschlager A., and Kiess W., “Stress, Stress Reduction and Obesity in Childhood and Adolescence,” Hormone Research in Pædiatrics 96, no. 1 (2023): 88–96, 10.1159/000519284. [DOI] [PubMed] [Google Scholar]
- 31. Boutelle K., Neumark‐Sztainer D., Story M., and Resnick M., “Weight Control Behaviors Among Obese, Overweight, and Nonoverweight Adolescents,” Journal of Pediatric Psychology 27, no. 6 (2002): 531–540, 10.1093/jpepsy/27.6.531. [DOI] [PubMed] [Google Scholar]
- 32. Croezen S., Visscher T. L., Ter Bogt N. C., Veling M. L., and Haveman‐Nies A., “Skipping Breakfast, Alcohol Consumption and Physical Inactivity as Risk Factors for Overweight and Obesity in Adolescents: Results of the E‐MOVO Project,” European Journal of Clinical Nutrition 63, no. 3 (2009): 405–412, 10.1038/sj.ejcn.1602950. [DOI] [PubMed] [Google Scholar]
- 33. Weitzman M., Cook S., Auinger P., et al., “Tobacco Smoke Exposure Is Associated With the Metabolic Syndrome in Adolescents,” Circulation 112, no. 6 (2005): 862–869, 10.1161/CIRCULATIONAHA.104.520650. [DOI] [PubMed] [Google Scholar]
- 34. Kim Y., Choi S., Chun C., Park S., Khang Y. H., and Oh K., “Data Resource Profile: The Korea Youth Risk Behavior Web‐Based Survey (KYRBS),” International Journal of Epidemiology 45, no. 4 (2016): 1076–1076e, 10.1093/ije/dyw070. [DOI] [PubMed] [Google Scholar]
- 35. Healy G. N., Clark B. K., Winkler E. A., Gardiner P. A., Brown W. J., and Matthews C. E., “Measurement of Adults' Sedentary Time in Population‐Based Studies,” American Journal of Preventive Medicine 41, no. 2 (2011): 216–227, 10.1016/j.amepre.2011.05.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Yamamoto K., Matsuda F., Matsukawa T., et al., “Identifying Characteristics of Indicators of Sedentary Behavior Using Objective Measurements,” Journal of Occupational Health 62, no. 1 (2020): e12089, 10.1002/1348-9585.12089. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Tremblay M. S., Carson V., Chaput J. P., et al., “Canadian 24‐Hour Movement Guidelines for Children and Youth: An Integration of Physical Activity, Sedentary Behaviour, and Sleep,” Applied Physiology, Nutrition, and Metabolism 41, no. 6 Suppl 3 (2016): S311–S327, 10.1139/apnm-2016-0151. [DOI] [PubMed] [Google Scholar]
- 38. Lee E. Y., Khan A., Uddin R., Lim E., and George L., “Six‐Year Trends and Intersectional Correlates of Meeting 24‐Hour Movement Guidelines Among South Korean Adolescents: Korea Youth Risk Behavior Surveys, 2013‐2018,” Journal of Sport and Health Science 12, no. 2 (2023): 255–265, 10.1016/j.jshs.2020.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Farooq A., Martin A., Janssen X., et al., “Longitudinal Changes in Moderate‐To‐Vigorous‐Intensity Physical Activity in Children and Adolescents: A Systematic Review and Meta‐Analysis,” Obesity Reviews 21, no. 1 (2020): e12953, 10.1111/obr.12953. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Kandola A., Lewis G., Osborn D. P. J., Stubbs B., and Hayes J. F., “Depressive Symptoms and Objectively Measured Physical Activity and Sedentary Behaviour Throughout Adolescence: A Prospective Cohort Study,” Lancet Psychiatry 7, no. 3 (2020): 262–271, 10.1016/S2215-0366(20)30034-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Rey‐Lopez J. P., Vicente‐Rodriguez G., Biosca M., and Moreno L. A., “Sedentary Behaviour and Obesity Development in Children and Adolescents,” Nutrition, Metabolism, and Cardiovascular Diseases 18, no. 3 (2008): 242–251, 10.1016/j.numecd.2007.07.008. [DOI] [PubMed] [Google Scholar]
- 42. Biddle S. J., Garcia Bengoechea E., and Wiesner G., “Sedentary Behaviour and Adiposity in Youth: A Systematic Review of Reviews and Analysis of Causality,” International Journal of Behavioral Nutrition and Physical Activity 14, no. 1 (2017): 43, 10.1186/s12966-017-0497-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Li C., Yuan Z., Clements‐Noelle K., Fu Y., Deadmond M., and Yang W., “Physical Activity and Overweight/Obesity Among Academic Stressed Adolescents,” Biostatistics and Epidemiology International Journal 1, no. 2 (2018): 40–46, 10.30881/beij.00011. [DOI] [Google Scholar]
- 44. Noh Y. M. Y. and Eun K., “The Influences of Academic Factors on Body Weight Loss Efforts Among Obese Adolescents,” Journal of East‐West Nursing Research 24, no. 1 (2018): 63–69, 10.14370/jewnr.2018.24.1.63. [DOI] [Google Scholar]
- 45. Ekelund U., Steene‐Johannessen J., Brown W. J., et al., “Does Physical Activity Attenuate, or Even Eliminate, the Detrimental Association of Sitting Time With Mortality? A Harmonised Meta‐Analysis of Data From More Than 1 Million Men and Women,” Lancet 388, no. 10051 (2016): 1302–1310, 10.1016/S0140-6736(16)30370-1. [DOI] [PubMed] [Google Scholar]
- 46. Goodpaster B. H. and Sparks L. M., “Metabolic Flexibility in Health and Disease,” Cell Metabolism 25, no. 5 (2017): 1027–1036, 10.1016/j.cmet.2017.04.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Hamilton M. T., Hamilton D. G., and Zderic T. W., “Role of Low Energy Expenditure and Sitting in Obesity, Metabolic Syndrome, Type 2 Diabetes, and Cardiovascular Disease,” Diabetes 56, no. 11 (2007): 2655–2667, 10.2337/db07-0882. [DOI] [PubMed] [Google Scholar]
- 48. Pearson N., Braithwaite R. E., Biddle S. J., van Sluijs E. M., and Atkin A. J., “Associations Between Sedentary Behaviour and Physical Activity in Children and Adolescents: A Meta‐Analysis,” Obesity Reviews 15, no. 8 (2014): 666–675, 10.1111/obr.12188. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Robinson T. N., Banda J. A., Hale L., et al., “Screen Media Exposure and Obesity in Children and Adolescents,” Pediatrics 140, no. Suppl 2 (2017): S97–S101, 10.1542/peds.2016-1758K. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figure S1: Odds ratio for obesity (def ined using the 2017 Korean Child and Adolescent Growth Charts) by combinations of meeting PA guidelines and ST guidelines.
Table S1: Independent association between physical activity, sedentary time, and obesity (defined using the 2017 Korean Child and Adolescent Growth Charts), stratified by grades.
Table S2: Independent association between physical activity, sedentary time, and obesity stratified by sex.
Table S3: Combined association between adherence of physical activity guidelines and sedentary time guidelines with obesity stratified by sex.
Data Availability Statement
The data that support the findings of this study are available in the Korea Disease Control and Prevention Agency at https://www.kdca.go.kr/. These data were derived from the following resources available in the public domain: Korea Youth Risk Behavior Web‐based Survey, https://www.kdca.go.kr/yhs/yhs/main.do.