Effects of Different Types of Exercise on Body Composition and Fat Distribution in HIV-Infected Patients: A Brief Review
Bibliographic record
Abstract
HIV infection and its treatment is associated with unfavourable metabolic and morphological abnormalities. These metabolic abnormalities, particularly alterations in body composition and fat distribution, may increase the risk for cardiovascular and metabolic complications, as well as reduce functional independence and lower self-esteem. Thus there is an urgent need to develop interventions intended to manage secondary side effects of HIV or antiretroviral therapy-related complications. In poly-treated patients, nonpharmacological interventions are a logical first step. Exercise training in particular may help alleviate some of the metabolic adverse effects associated with antiretroviral therapy by favourably altering body composition and patterns of body fat distribution. Studies have shown that exercise training, particularly aerobic training, can help reduce total body and visceral fat, as well as normalizing lipid profiles in HIV-infected patients. The results for resistance training, however, are less conclusive. Knowledge of the use of resistance and aerobic training and its attendant effects on insulin resistance and adipocytokines may represent an effective nonpharmacologic means for treating metabolic complications of HIV-infected persons who are receiving appropriate antiretroviral therapy. In this brief review we examine the effects of aerobic and resistance training on body composition, body fat distribution, and selected metabolic outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".