Association of Physical Activity and Bone Mineral Density in Adults with Depressive Symptoms
Bibliographic record
Abstract
Abstract Depression affects a significant proportion of adults in the United States. Studies exploring the association between depression and bone mineral density (BMD) have shown mixed results. Moreover, the relationship between BMD and physical activity (PA) in individuals with depressive symptoms is unknown. In this paper, we evaluated the association of depressive symptoms and PA with BMD, as well as difference in BMD among females with depressive symptoms before and after menopause. Data from the 2011–2018 National Health and Nutrition Examination Survey were used. Multivariable linear regression was used to explore the relationship between BMD and exposure variables. The study included 9,238 participants, of whom 766 had depressive symptoms. The presence and severity of depressive symptoms were significantly associated with lower BMD (aCoef.=−0.0200 for depressive symptoms, −0.0017 for depressive symptom severity; p<0.001). Vigorous PA intensity was positively correlated with BMD, with and without controlling for depressive symptoms (aCoef.=0.0006; CI=[0.0003, 0.0008]; p<0.001). Additionally, high levels of vigorous PA showed a significant positive relationship with BMD (aCoef.=0.0141; CI=[0.0078, 0.0205]; p<0.001). Postmenopausal status was significantly associated with lower BMD. No significant interaction effects were observed between depressive symptoms and PA or menopausal status on BMD. Our study demonstrated the an association between depressive symptoms and low BMD, as well as a positive association between high-intensity vigorous PA and BMD. Future studies should aim to replicate our findings and evaluate the underlying mechanisms.
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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 | 0.000 | 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 | 0.001 |
| 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".