Cognitive functioning, fat mass and physical activity in young adults
Bibliographic record
Abstract
Introduction: Evidence suggests an association between excess weight and low cognitive performance; however, findings are inconsistent due to variations in measurement approaches. Further research is needed to explore this link, considering factors such as physical activity and education level. Objective: this study aimed to: (a) identify possible differences in cognitive performance between participants with high versus normal fat mass levels, and (b) assess the effects of fat mass levels and physical activity on executive and cognitive-motor interference performance. Methodology: A non-experimental design was conducted, involving 61 young adult participants (Mage=18.9, SD=1.9) who underwent evaluations for executive functioning, cognitive-motor interference in dual tasks, body composition, and physical activity. Results: Although differences favoring participants with high fat mass levels were found, they were not statistically significant. These results remained consistent regardless of physical activity level. Discussion: While some studies have found an association between high adiposity and lower cognitive performance, others have not detected this relationship. Our findings align with the latter, emphasizing the need for future studies to include mediating variables to better understand this complex association. Conclusion: this investigation supports the notion that individuals with overweight and obesity do not exhibit inferior executive and cognitive-motor interaction performance compared to individuals with normal fat mass levels.
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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) | 0.000 | 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) | 0.003 | 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".