Association between Objective and Subjective Sleep Parameters with Postural Control Responses among Brazilian Schoolteachers
Bibliographic record
Abstract
Abstract Objective To investigate the impact of sleep quality on postural control in teachers. Methods Cross-sectional study with 41 schoolteachers (mean age 45.7 ± 10.4 years). Sleep quality was assessed in two ways: objectively (through actigraphy), and subjectively (through the Pittsburgh Sleep Quality Index). Postural control was assessed in an upright posture during 3 trials of 30s (bipedal and semitandem stances in rigid and foam surfaces with eyes open) with a period of rest across trials, on a force platform, based in the center of pressure measurements in the anteroposterior and mediolateral directions. Results The prevalence of poor sleep quality in this study sample was 53.7% (n = 22). No differences were found between Poor and Good sleep in the posturographic parameters (p > 0.05). Although, there was moderate correlation between postural control in the semitandem stance and subjective sleep efficiency for center of pressure area (rs = -0.424; p = 0.006) and amplitude in anteroposterior direction (rs = -0.386; p = 0.013). Discussion There is correlation between poor sleep quality and postural control in schoolteachers, as sleep efficiency decreases, postural sway increases. Poor sleep quality and postural control were investigated in other populations, but not in teachers. Several factors such as work overload, insufficient time for physical activities, among others, can contribute to a worse perception of sleep quality, as well as deterioration in postural control. Further studies with larger populations are needed to confirm these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".