A comparison of veteran athletes and sedentary individuals in terms of dynamic-static balance and cognitive functions
Bibliographic record
Abstract
There is strong evidence that being physically active has positive effects on healthy aging, primary and secondary prevention of the development of chronic diseases. The physical and cognitive health benefits of having an active past are less well-known. Therefore, this study aimed to compare veteran athletes with an active sports history with their sedentary peers in terms of balance and cognitive function. Fourteen veteran soccer players and 15 sedentary male volunteers aged between 35-55 years participated in the study. The demographic characteristics of the participants were questioned with a case report form. Participants' balance was evaluated statically and dynamically. Static balance was assessed using the Flamingo test and dynamic balance was assessed with the Y Balance test. The Montreal Cognitive Assessment (MoCA) was used to test cognitive functions. There was no difference between Veteran Athletes (VA) and Sedentary Participants (SP) in terms of static balance test scores with both right and left sides (p>0.05). The results of the VA group were significantly higher (p<0.05) in terms of the total scores of the Y balance test, which consisted of the average of the anterior (ANT), posterolateral (PL), and posteromedial (PM) reaches (p<0.05). In terms of MoCA score, the VA group obtained significantly higher results compared to the SP group (p<0.05). Veteran athletes were found to be superior to same-aged sedentary in terms of dynamic balance and cognitive function. Having an active sporting background can help to mitigate the loss of physical skills such as balance and cognitive functioning that inevitably occurs during the natural aging process. In addition, active old age is also crucial for the preservation of these skills.
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 | 0.001 | 0.000 |
| 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".