Comparison of concurrent, resistance, or aerobic training on body fat loss: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This systematic review and meta-analysis compared the differential effects of resistance training (RT), aerobic training (AT), and concurrent training (CT) on body mass and body fat loss in metabolically healthy individuals. METHODS: A systematic search of PubMed, SportDiscus, and Web of Science databases for randomized controlled trials published between January 1980 and January 2023, comparing RT, AT, and CT in healthy adults was conducted. Primary outcomes of interest included changes in fat mass and body fat percentage; secondary outcomes were body mass and fat-free mass (FFM). Sub-analyses on intervention duration (< or ≥ 10 weeks), CT timing (aerobic and resistance exercises done on the same day versus different days within a week), and workload matching (equating workloads between AT, RT, and CT), were conducted. Study protocols followed PRISMA 2020 guidelines and were pre-registered on PROSPERO (CRD42023396530). RESULTS: = 1) when comparing RT to different-day CT (aerobic and resistance exercises done on different days within a week). CONCLUSIONS: While there are no differences in percent body fat loss between exercise modes, AT and CT are more effective than RT alone in reducing absolute fat mass; however, RT neither improved nor impeded fat mass loss when incorporated into CT. Combining aerobic and resistance exercises on the same-day or different-day does not appear to influence the effectiveness of CT. When exercise interventions are short in duration (<10 weeks), there does not appear to be a difference in fat loss between exercise modalities. These results support the concurrent use of aerobic and resistance exercises for fat mass reduction, as well as an emphasis on workload and duration when programming exercise for fat loss.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".