National trends in physical activity among adolescents in South Korea before and during the COVID‐19 pandemic, 2009−2021
Bibliographic record
Abstract
Abstract With the ongoing COVID‐19 pandemic, several previous studies from different countries showed that physical activity (PA) decreased during the COVID‐19 outbreak. However, few studies have examined the recent tendency of PA in the adolescent population. Thus, we aimed to investigate the long‐term trend of PA in Korean youth and the prevalence changes between before and during the COVID‐19 pandemic. Data from Korea Youth Risk Behavior Web‐Based Survey (KYRBS) was collected for consecutive years between 2009 and 2021. The period was separated into prepandemic (2009−2019), early‐pandemic (2020), and mid‐pandemic (2021). Self‐reported amount of PA was categorized into four groups (insufficient, aerobic, muscle strengthening, and both physical activities) according to World Health Organization (WHO) PA guidelines. A total of 840 488 adolescents aged 12−18 who fully responded to the survey were selected (response rate: 95.2%). The 13‐year trends in the proportion of adolescents who reported aerobic and muscle‐strengthening activities met or exceeded 2020 WHO exercise guidelines for adolescents plateaued (11.9% from 2009 to 2011, 14.2% from 2018 to 2019, 14.4% from 2020, and 14.0% from 2021); however, the slope decreased during the pandemic (βdiff, −0.076; 95% confidence interval [CI], −0.123 to −0.029). Proportion of sufficient aerobic exercise among adolescents sharply decreased midst the pandemic (28.0% from 2009 to 2011, 29.4% from 2018 to 2019, and 23.8% from 2020; βdiff, −0.266; 95% CI, −0.306 to −0.226) but increased again in 2021 (26.0% from mid‐COVID 19; 95% CI, 25.4−26.7). Similar patterns were observed in Metabolic Equivalent Task (MET) score (MET‐min/week; 804.1 from 2018 to 2019, 720.9 from 2020, and 779.6 from 2021). The mean difference in MET score between pre‐COVID and post‐COVID was −55.4 MET‐min/week (95% CI, −70.5 to −40.3). Through a nationwide representative study, there was no significant difference with regard to the number of Korean adolescents who achieved the PA guidelines (pre and postpandemic); however, the prevalence of recommended levels of PA needs to increase more based on the trend before the COVID‐19 outbreak. The findings of this study suggest reinforcement of the importance of public health policies for Korean youths to be more physically active, especially during and after the pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".