Assessing the Impact of COVID-19 on Amateur Runners’ Performance: An Analysis through Monitoring Devices
Bibliographic record
Abstract
This retrospective study aimed to analyze the return to running of non-professional runners after experiencing asymptomatic or mild COVID-19. Participants aged 18-55 years who maintained a training load of ≥10 km/week for at least three months prior to diagnosis and utilized Garmin/Polar apps were included. From these devices, parameters such as pace, distance, total running time, cadence, and heart rate were collected at three intervals: pre-COVID, immediately post-COVID, and three months after diagnosis. The Wilcoxon signed rank test was used for analysis (significance was set at ≤0.05). Twenty-one participants (57.1% male; mean age 35.0 ± 9.8 years) were included. The results revealed a significant decrease in running duration and distance two weeks after diagnosis, without significant changes in other parameters. Three months after infection, no differences were observed compared to pre-infection data, indicating a return to the pre-disease training load. These findings underscore the transient impact of COVID-19 on training performance among non-professional runners with mild or asymptomatic symptoms, highlighting the importance of tailored strategies for resuming running after infection.
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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) | 0.001 | 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 | 0.000 | 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".