The Benefits of High Intensity Exercise on the Brain of a Drug Abuser
Bibliographic record
Abstract
Chronic drug abuse has been shown to cause dysfunctions on the frontal lobe and affect cognition, cardiac autonomic control and psychosocial aspects. Despite physical exercise has been shown to improve cerebral functioning, the effects of a high intensity exercise training program needs to be further explored in a drug abuse condition. The patient was a 32-year-old male who has been an alcohol and crack/cocaine user for 20 years. The high intensity exercise training protocol consisted of four 30-second “all-out” bouts performed three times per week during four weeks. The participant had electroencephalographic (EEG) activity, cognition, cardiac autonomic control and psychosocial questionnaires evaluated before and after high intensity exercise training. Prefrontal cortex (PFC) oxygenation during an incremental running exercise test was also recorded. EEG topographical analysis revealed greater PFC activation during the cognitive test. Performance on the cognitive test was enhanced (l number of total errors and reaction time). Parasympathetic cardiac indices, including RMSSD, SDNN, Pnn50% and HF power increased by 77.4%, 83.3%, 57.7% and 293.2%, respectively. Sleep quality increased 23% and anxiety levels decreased 52.6%. Psychological and social domains increased 5.3% and 13.7%, respectively. In addition, incremental treadmill running time increased 12.5% and PFC oxyhemoglobin increased 228.2% at the beginning of the treadmill test, 305.4% at the middle and 359.4% at the end of the test. Thus, high intensity exercise training improved PFC functioning, cardiac autonomic control and psychological parameters. These results might indicate high intensity exercise as an alternative and non-pharmacological tool to help the rehabilitation of a drug abuser.
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 | 0.000 |
| 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 | 0.000 | 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".