Physical Activity Status and Related Factors among Middle-Aged Women in West of Iran, Hamadan: A Cross-Sectional Study
Bibliographic record
Abstract
Physical inactivity is a major health problem in developing countries. Regular Physical Activity (RPA) can reduce the risk of many diseases such as cardiovascular disease, diabetes, and obesity that are prevalent in middle and old ages specifically in women. The (RPA) status among middle-aged Iranian women is not well known. The purpose of this study was to investigate the physical activity status and related factors among middle-aged women in Hamadan, a city in western Iran. The participants of this cross-sectional study were comprised of 866 middle-aged women in Hamadan who were selected using a proportional stratified random sampling method in 2015. The participants completed a self-administered questionnaire containing demographic characteristics and an International Physical Activity Questionnaire-Short Form (IPAQ-S).The data were analyzed with SPSS-16 software using Multi- nominal Logistic Regression. The results revealed that about 57% of the study population was inactive or not sufficiently active (light level). Additionally, the results showed that less than a quarter of the study participants (19.3%) exhibited a severe level of physical activity. The associations between RPA and age, education level and job were significant (P<0.05). The chi-square test revealed a significant difference in RPA with regards to residency locations (P<0.05). The demographic variables relationship with physical activity appears to be important and these findings can be a prelude to design of effective intervention strategies in promoting physical activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".