Associations Between Classical Music, Physical Activity And Symptoms Of Depression In Older Adults During The Covid-19 Pandemic
Bibliographic record
Abstract
Associations between Classical Music, Physical Activity andSymptoms of Depression in Older Adults during the COVID-19 Pandemic Naomi A. Arnold-Nedimala1, Daniel D. Callow1,2, Gabriel Pena1, Zofia Cieslak1, Hannah Lipson1, Tyrese Brown1, Yanmin Qu1, Yash Kommula1,2, John Woodard3, and J. Carson Smith, FACSM1,2,󠄀▯ Department of Kinesiology, University of Maryland, College Park, MD, USA2 Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA3 Department of Psychology, Wayne State University, Detroit, MI. PURPOSE: The global impact of COVID-19 continues to be a focused area of research. The elderly population was greatly affected by the lockdown as it strongly discouraged physically interacting with family and friends. The neurological benefits of listening to classical music and engaging in physical activity are emerging areas of research. The aims of this study were to understand the independent effect that listening to classical music and maintaining physical activity levels had in attenuating symptoms of depression in older adults (50 - 90+) during the initial COVID-19 pandemic lock-down. METHODS: A survey including the Geriatric Depression Scale (GDS), the Physical Activity Scale for the Elderly (PASE), and questions about listening to music (Classical, Broadway, Light Rock,Country, Rock, Christian, etc.), was generated and distributed to people living in the United States and Canada, immediately following the initial COVID-19 lockdown in April 2020. An analysis of covariance was then employed to determine the independent associations between PASE and listening to classical music with GDS scores while controlling for age, sex, education, and music listening frequency. RESULTS: In our sample of n = 875 older adults, we report that both listening to classical music(CML) (n = 860, F(1,860) = 10.8, d = 0.247, p = .001) and higher levels of physical activity (F(1,860) = 41.8, p < .001) were independently associated with lower symptoms of depression. CONCLUSION: These results suggest that both listening to classical music and higher levels of physical activity may provide independent and additive benefits for symptoms of depression in older adults during periods of physical and social isolation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".