The impact of the COVID-19 pandemic on the diet, training habits and fitness of Masters cyclists
Bibliographic record
Abstract
Background: The number of Masters-level athletes (≥ 35 years of age) taking part in cycling has increased in the past years which may have beneficial effects on their health. The restrictions brought on by the COVID-19 pandemic has the potential to negatively impact the diet, training and fitness of these individuals due to restrictions in place to slow the spread of the virus. Aim: To investigate how the COVID-19 pandemic impacted the diet, training and fitness of Masters-level cyclists. Methods: 32 Masters cyclists (12 males, 20 females; mean age 47 ± 10 years) completed two incremental exercise tests one month apart during the pandemic to assess sport-specific fitness. Participants also completed online questionnaires to report their sedentary behavior and dietary intake before and during the pandemic, and their training volume and intensity for a specified week in February (before the pandemic) and each of March, April and May (during the pandemic). Results: No differences were seen in fitness ( p = 0.6), training volume ( p = 0.24) or intensity ( p = 0.79) and sedentary behavior ( p = 0.14) during the pandemic. Energy intake was unchanged ( p = 1.0) during the pandemic, but participants consumed lower amounts of key nutrients such as fiber, vitamin A, omega-3 fatty acids and potassium ( p < 0.05) while consuming more alcohol ( p = 0.008) and vitamin C ( p = 0.03). Conclusions: Our data shows that the COVID-19 pandemic has undesirable effects on nutrient and alcohol intake of Masters cyclists without impacting their training regimes, which may have adverse effects on their overall health and fitness in the long term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".