Longitudinal Shifts in Movement Behaviors during the COVID-19 Pandemic: Relations to Posttraumatic Stress Disorder among University Students
Bibliographic record
Abstract
This study examined the longitudinal changes of movement behaviors and their relationships with posttraumatic stress disorder (PTSD) among university students during the coronavirus disease 2019 in China. A total of 569 university students completed online surveys twice (Time 1: between December 2020 and January 2021; Time 2: between November and December 2021). Physical activity, sedentary behavior, sleep duration and quality, as well as PTSD were self-reported. According to Canadian 24-h movement guidelines, the longitudinal shifts in each movement behavior from Time 1 to Time 2 were divided into four categories (consistently meeting the guidelines, from meeting to not meeting the guidelines, from not meeting to meeting the guidelines, and consistently not meeting the guidelines). Generalized linear mixed models were conducted using 410 valid responses (20.2 ± 1.0 years old at Time 2, 41.2% males). From Time 1 to Time 2, 22.2%, 2.0%, and 45.6% of the students consistently met the physical activity, sedentary behavior, and sleep guidelines, respectively. Compared to those who consistently met the sedentary behavior guideline, students who consistently failed to meet or changed from meeting to not meeting the guidelines had higher levels of PTSD. Students who had poor sleep quality at both time points or changed from good to bad sleep quality had higher levels of PTSD than those who maintained good sleep quality over time. Compared to those who consistently failed to meet the guideline, students who consistently met the PA guideline had higher levels of PTSD. These findings highlight the needs to improve and maintain healthy behaviors for mental health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".