Epilepsy, Physical Activity and Sports: A Narrative Review
Bibliographic record
Abstract
People with epilepsy (PWE) are less physically active compared with the general population. Explanations include prejudice, overprotection, unawareness, stigma, fear of seizure induction and lack of knowledge of health professionals. At present, there is no consensus on the role of exercise in epilepsy. This paper reviews the current evidence surrounding the risks and benefits associated with physical activity (PA) in this group of patients. In the last decade, several publications indicate significant benefits in physiological and psychological health parameters, including mood and cognition, physical conditioning, social interaction, quality of life, as well as potential prevention of seizure presentation. Moreover, experimental studies suggest that PA provides mechanisms of neuronal protection, related to biochemical and structural changes including release of β-endorphins and steroids, which may exert an inhibitory effect on the occurrence of abnormal electrical activity. Epileptic discharges can decrease or disappear during exercise, which may translate into reduced seizure recurrence. In some patients, exercise may precipitate seizures. Available evidence suggests that PA should be encouraged in PWE in order to promote wellbeing and quality of life. There is a need for prospective randomized controlled studies that provide stronger clinical evidence before definitive recommendations can be made.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".