Physical Activity, Sleep Quality, and Musculoskeletal Pain in University Students: A Comparison of Academic and Exam Periods
Bibliographic record
Abstract
Purpose This study aimed to examine physical activity levels, sleep quality, and musculoskeletal discomfort in university students during the academic and exam periods. Approach or Design A cross-sectional comparative design was used. Setting The study was conducted at a University. Participants A total of 227 ( n = 199 female) undergraduate students participated. Method Data were collected face-to-face using the Cornell Musculoskeletal Discomfort Questionnaire, Pittsburgh Sleep Quality Index (PSQI), and International Physical Activity Questionnaire-Short Form (IPAQ-SF) during both periods. Daily sitting durations and preferred study postures were also recorded. Data were analyzed using paired t-tests, with significance set at P < 0.05. Results During the exam period, musculoskeletal discomfort was highest in the back, lower back, and neck regions. PSQI scores showed worse subjective sleep quality, longer sleep latency, shorter duration, and increased disturbances during exams ( P < 0.05). IPAQ-SF results indicated reduced physical activity and significantly increased daily sitting time in the exam period ( P < 0.05). Conclusion University students experience more musculoskeletal discomfort, poorer sleep quality, and decreased physical activity during exams. These findings highlight the need for preventive strategies during high-stress academic periods.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".