Assessing the damage: analyzing the impact of the COVID-19 pandemic on accelerometer-assessed 24-hour movement behaviors in Brazilian adolescents
Bibliographic record
Abstract
Background: Although there is consistent evidence of unhealthy changes in the 24-hour movement behaviours when comparing pre-COVID-19 periods to the early stages of the pandemic, there is limited research on long-term changes among adolescents. This study aimed to analyze both between- and within-participant differences in accelerometer-assessed 24-hour movement behaviours by comparing cross-sectional and prospective data from the pre-COVID-19 period (August to December 2019) to the period following the reopening of schools for in-person classes in southern Brazil (August to December 2022). Methods: This is a repeated cross-sectional with a nested cohort study. The 24-hour movement behaviours (i.e., time spent in physical activity of light [LIPA] and moderate-to-vigorous [MVPA] intensities, sedentary behaviour [SB], and sleep time [SPT]) were assessed by processing raw accelerometer data derived from a 24-hour/7-day wrist-worn protocol. Compositional multilevel models were applied to compare the 24-hour movement behaviour composition between time points for both cross-sectional and prospective data. Self-reported sociodemographic characteristics were examined as potential moderators. Results: The cross-sectional and prospective samples comprised, respectively, 1276 (53% female, average age of 16.4 ± 1.1) and 249 (53% female, average age of 15.6 ± 0.8) participants. The 24-hour movement behaviour composition differed between time-points in the cross-sectional (p<0.001) and prospective samples (p<0.001). Differences from 2019 to 2022 were explained by lower MVPA (-3.3 and -5.4 min/day in cross-sectional and prospective analysis, respectively) and a higher SB (4.7 and 34 min/day in cross-sectional and prospective analysis, respectively). No significant differences were observed for LIPA and SPT. Conclusions: Differences in the 24-hour movement behaviour composition comparing the cross-sectional samples, although statistically significant, were considered trivial and unlikely to have a substantial practical impact. However, considerable differences were observed in the prospective analysis. Taken together, the results suggest that most of the observed changes over time were expected as a natural consequence of aging during high school, with only a small portion attributable to the residual impact of 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".