Impact of Aerobic Training on Cognitive Function and Quality of Life in Post Covid-19 Patients
Bibliographic record
Abstract
Background: Cognitive deficits among COVID-19 survi-vors are currently reported by few studies. These studies report cognitive deficits in mild and moderate cases of the infection, with significant impairment of executive functions, memory, and attention. In addition, cognitive deficits have been reported in studies performed in the acute phase of the infection and in recovered patients. Aim of Study: To determine the therapeutic effect of aerobic training on cognitive function and quality of life in post covid-19 patients. Patients and Methods: A referred diagnosed thirty post covid-19 patients with mild cognitive impairment from both genders, with age ranged from (45-55) years old, were ran-domly assigned to equal two groups: A study group and a control group. Study group received aerobic training on a folding pedal machine in addition to cognitive rehabilitation on a commercially available computerized cognitive training platform (CogniFitTM) and control group received only cog-nitive rehabilitation on a commercially available computerized cognitive training platform (CogniFitTM) for 18 sessions every other day for 6 weeks, 3 sessions/week, each session for 50 minutes. All patients were evaluated with the Mini Mental State Examination (MMSE) and the Montreal Cognitive assessment (MoCA) scale and the quality of life was assessed by the quality of life (QoL) scale. The study was done in out-patient clinics of Abu Kier general Hospital. Results: The study revealed that there is a significant difference between study and control groups as the p-value was (0.001) which indicated that study group shows improve-ment in cognitive functions and quality of life style more than control group. Conclusion: The study revealed that six weeks of aerobic training combined with cognitive rehabilitation on a commer-cially available computerized cognitive training platform (CogniFitTM) for patients was a beneficial approach and improve cognitive dysfunction and quality of life in post covid-19 patients.
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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 | 0.000 | 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 | 0.000 | 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".