Musculoskeletal Pain Among Community-Dwelling Older Adults During the COVID-19 Pandemic: A Longitudinal Telesurvey
Bibliographic record
Abstract
Purpose: To describe the musculoskeletal (MSK) pain prevalence and incidence and identify factors associated with MSK pain among older adults over a 1-year follow-up during the COVID-19 pandemic. Method: This longitudinal telesurvey recruited community-dwelling older adults (≤65 years) in Hamilton, Ontario, Canada. MSK pain prevalence and incidence were calculated. Multilevel negative binomial and ordered logistic regression models were used to identify factors associated with the number of pain sites (0 to 7 pain sites), and most intense pain (no, mild, moderate, and severe pain). Results: We included 247 participants. Pain prevalence ranged from 64% at baseline to 73% at 1 year. Being older (IRR [incidence rate ratio]: 0.96; 95% CI: 0.94 to 0.98) and having better mobility (IRR: 0.96; 0.95 to 0.96) were associated with fewer number of pain sites. Being male (OR 0.87; 0.82 to 0.93) was associated with a lower pain intensity. While having a greater BMI (OR 1.07; 1.00 to 1.14), being older at 12 months (OR 1.09; 1.01 to 1.18), having better mobility at 12 months (OR 1.06; 1.01 to 1.11), and better mental health at 9 (OR 5.32; 1.25 to 22.64) and 12 months (OR 7.19; 1.71 to 30.18) were associated with a higher pain intensity. Conclusions: Older adults had high MSK pain prevalence during the first year of the COVID-19 pandemic; however, there was not a significant increase over time. Our results demonstrated that better mobility, being older, greater BMI, being female, and having better mental health are important factors associated with MSK pain in older adults in the COVID-19 scenario.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".