Physical activity and sedentariness levels in patients with post-exertional malaise resulting from post-COVID-19 syndrome
Bibliographic record
Abstract
BackgroundPost-exertional malaise (PEM) is a complex phenomenon characterized by extreme fatigue, reduced endurance, and muscular and joint pains. Physical activity (PA) has recognized health benefits, including reducing the risks of chronic diseases and mortality. During the pandemic, a general decline in PA was measured, but the profile of the various components of PA and sedentariness in patients with PEM resulting from post-COVID-19 syndrome (PCS-19) remains scarce. It is relevant to observe the impact of these discomforts on PQ after their occurrence.ObjectiveThis study examines the detailed PA and sedentary profile of individuals affected by PEM associated with PCS-19.MethodsAn online questionnaire disseminated via social media platform evaluated PA and sedentariness before and after COVID-19 diagnostic.ResultsIndividuals with PEM (n = 154) became more sedentary and inactive post-COVID-19. Specifically, PA at work decreased in women and those whose last infection occurred over a year ago. Walk decreased for women but increased for men. Bike journeys generally decreased after COVID-19. The severity of PEM, the pace of recovery, and fear of malaise influenced PA changes.ConclusionsThe PCS-19 leads to increased sedentary behavior and a decline in PA, particularly at work, and is more pronounced among women and those more severely affected by PEM. These findings are critical for post-COVID PA resumption, including for workers who go back to work and who regain normal duties while being potentially deconditioned.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".