Relationships between Bone Mineral Density and Antidepressant Use and Physical Activity in U.S. Adults
Bibliographic record
Abstract
Introduction: Osteoporosis is a major public health concern that affects millions of people worldwide. It is a progressive disease characterized by low bone mass and deterioration of bone tissue, leading to an increased risk of fractures. Antidepressant use and physical activity have been suggested as potential modifiable factors that may affect bone mineral density (BMD). However, the relationship between these factors and BMD is not fully understood, and it is uncertain whether these relationships differ for males and females. Methods: Using data from the 2011-2018 National Health and Nutrition Examination Surveys, we examined BMD differences in groups among 7,254 adults. Analyses were first conducted by using the Anderson-Darling normality test, T-test, and the Wilcoxon rank-sum test, and then used multivariate linear regressions to investigate its potential associations between antidepressant use and physical activity along with other factors. Results: Excluding the underweight adults, there is significant difference in BMD between those with and without depression in the female group (p=0.03), but not in the male group (p=0.54). No significant difference was found between those who used and those who did not use antidepressants, in either group, female (p=0.96) and male (p=0.77). Antidepressant use and low-level physical activity intensity were associated with decreased BMD values (Mean [SD] = -0.0073 [0.0049]). Discussion: This study suggests that physical activity may be a potential modifiable factor that could help prevent osteoporosis, particularly in females. On the other hand, antidepressant use, and low-level physical activity may increase the risk of low BMD. These findings highlight the importance of lifestyle modifications in the prevention of negative impact of low BMD. Further studies are needed to explore the impact of different types of antidepressant use in BMD.
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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 | 0.001 |
| 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".