Sociodemographic and mental health characteristics associated with changes in movement behaviours due to the COVID-19 pandemic in adolescents
Bibliographic record
Abstract
Objectives: Control measures enacted to control the spread of COVID-19 appear to have impacted adolescent movement behaviours. It remains unclear how these changes relate to sociodemographic characteristics and indicators of mental health. Understanding these relationships can contribute to informing health promotion efforts. The purpose of this study is to examine sociodemographic and mental health characteristics associated with changes in movement behaviours (physical activity, screen time, sleep duration) due to the COVID-19 pandemic among adolescents. Methods: tests, and estimation of effect sizes using Cohen's d and h tests were performed between self-reported perceived changes (increase; decrease; no change) to physical activity, TV watching, social media use, and sleep duration as a result of the COVID-19 pandemic and gender, age, race/ethnicity, income, depression and anxiety symptoms, flourishing-languishing, and self-rated mental health. Results: Over half of students reported increases in TV viewing and social media use and approximately 40% reported decrease in physical activity and increase in sleep duration due to the COVID-19 pandemic. More females (68.9%) than males (54.3%) reported increase in social media use (Cohen's h ≥ 0.2-0.5). No change from pre-COVID-19 social media use and sleep duration were associated with fewer depression and anxiety symptoms and better self-rated mental health compared to reports of an increase or decrease. These effect sizes ranged from small-to-moderate to moderate-to-large (Cohen's d/h ≥ 0.2-0.8). Decreased physical activity and sleep duration were associated with better psychological functioning with effects sizes of small-to-moderate. Compared to an increase or no change, decreased sleep had the largest effect size of less frequent depression symptoms (Cohen's d ≥ 0.5-0.8). Conclusion: Maintaining pre-COVID-19 screen time and sleep duration during early stages of the COVID-19 lockdown was generally beneficial to mental health, with sleep being particularly important in regards to symptoms of depression. Psychological functioning was more related to physical activity and sleep than screen time during 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".