Musculoskeletal Pain Among Community-Dwelling Older Adults during the COVID-19 Pandemic: A Longitudinal Survey
Bibliographic record
Abstract
OBJECTIVE: To describe the 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. METHODS: This longitudinal telesurvey recruited community-dwelling older adults (less than or equal to 65 years) in Hamilton, 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 between 64% at baseline to 73% at one year. The interaction of mental health by time as well as age and mobility were associated with the number of pain sites. Being older (IRR 0.96; 95% CI 0.94 to 0.98) and having better mobility (IRR 0.96; 0.95 to 0.96) were associated with lower number of pain sites. Having better mental health was associated with higher numbers of pain sites at 6- (IRR 1.58, 95% CI 1.05 to 2.37), 9- (IRR 1.55, 95% CI 1.02 to 2.34), and 12-months follow-ups (IRR 1.66, 95% CI 1.10 to 2.53). Sex, BMI and interactions of age by time, mobility by time, and mental health by time interaction were associated with the most intense pain. Being male (OR 0.87; 0.82 to 0.93) and having a greater BMI (OR 1.07; 1.00 to 1.14) were associated with lower and higher pain intensity, respectively. Being older (OR 1.09; 1.01 to 1.18) and having better mobility (OR 1.06; 1.01 to 1.11) were associated with higher pain intensity at 12 months. CONCLUSION: Older adults were found to have high MSK pain prevalence, however, there was not a significant increase over time. Our results demonstrated that mobility, age, BMI, sex, and mental health are important factors associated with MSK pain in older adults.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".