Trajectories of pain and depressive symptoms among people living with low back pain during the COVID-19 pandemic: a 24-month longitudinal study
Bibliographic record
Abstract
Abstract Introduction: We explored trajectories of pain intensity and depressive symptoms over the first 24 months of the pandemic in people with low back pain. Methods: This longitudinal study was conducted alongside the Quebec Low Back Pain Study. Starting in April 2020 and every 3 months until July 2022, 291 participants completed an online survey. Group-based trajectory modeling was used to identify patterns of pain intensity and depressive symptoms. Onset outbreak characteristics were then put in relation with trajectory groups using multivariate logistic regression. Results: The analysis revealed 5 trajectories of pain intensity and depressive symptoms, respectively. The pain trajectories were stable mild (n = 17, 5.8%); stable moderate (n = 103, 35.4%); stable severe (n = 81, 27.8%); U-shape (n = 24, 8.3%), and inverted U-shape (n = 66, 22.7%). The trajectories of depressive symptoms were stable none (n = 58, 19.9%); stable very mild (n = 61, 21.0%); stable mild (n = 85, 29.2%); stable moderate (n = 59, 21.7%); and severe slightly improving (n = 24, 8.3%). Pre-COVID everyday/nearly everyday pain, average pain intensity, and widespread bodily pain were predictive of pain trajectory groups. Higher pre-COVID depression, acute stress disorder, and lockdown measures-related stress were associated with moderate/severe depressive trajectories. Discussion: Our findings indicated relative stability of pain and depressive symptoms among participants during the COVID-19 pandemic but also highlighted subgroups of people who experienced temporary deterioration or improvement over the first months of the pandemic that then reverted back to baseline levels. Modifiable risk factors were identified before the onset of the pandemic, which could give preventive measures in targeted populations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".