Association between sleep pattern and body mass index among undergraduate health colleges’ students at Qassim University, Saudi Arabia
Bibliographic record
Abstract
Background and objective: Better quality and adequate amount of sleep are essential to have better cognitive performance and by contrast, poor bedrest is related to a diminishing of cognitive and psychological functioning and deteriorating physical health. Recently the quality of sleep among various segments of the population has become a focus of continuous research and investigations, where insufficient sleep may lead to adverse cardio-metabolic effects by influencing body weight, blood pressure, and glucose tolerance. The objective of this study is to explore the associations between sleep patterns and Body Mass Index (BMI) among undergraduate female students in health colleges.Methods: Across-sectional study was utilized among 288 of undergraduate female students who represent 30% of all undergraduate students in health Colleges of female section, Qassim University. The data regarding their sleeping pattern was collected and calculated by using a validated self-report questioner (Pittsburgh Sleep Quality Index) and Body Mass.Results: According to PSQI scoring, both categories good and poor sleep maximum according to PSQI scoring; both categories good sleep and poor sleep maximum participant's BMI was normal but no statistically significant association was seen between BMI of poor sleep was higher in overweight and obese participants from all departments and also no statistically significant association was seen between poor sleep and body mass index of participant As for the sleep quality as a single question and sleep duration in relation to BMI for different specialties, only for nursing students there was a statistically significant difference of sleep quality/duration in relation to their BMI.Conclusions: Frequency of poor sleep was higher in overweight and obese participants but as per findings there was no statistically significant association was detected between PSQI score and BMI of participants.
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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 | 0.001 |
| 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 | 0.000 |
| 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".