Effects of detraining on left ventricular mass in endurance-trained individuals: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: Detraining refers to a loss of training adaptations resulting from reductions in training stimulus due to illness, injury, or active recovery breaks in a training cycle and is associated with a reduction in left ventricular mass (LVM). The purpose of this study was to conduct a systematic review and meta-analysis to determine the influence of detraining on LVM in endurance-trained, healthy individuals. METHODS AND RESULTS: Using electronic databases (e.g. EMBASE and MEDLINE), a literature search was performed looking for prospective detraining studies in humans. Inclusion criteria were adults, endurance-trained individuals with no known chronic disease, detraining intervention >1 week, and pre- and post-detraining LVM reported. A pooled statistic for random effects was used to assess changes in LVM with detraining. Fifteen investigations (19 analyses) with a total of 196 participants (ages 18-55 years, 15% female) met inclusion criteria, with detraining ranging between 1.4 and 15 weeks. The meta-analysis revealed a significant reduction in LVM with detraining (standardized mean difference = -0.586; 95% confidence interval = -0.817, -0.355; P < 0.001). Independently, length of detraining was not correlated with the change in LVM. However, a meta-regression model revealed length of the detraining, when training status was accounted for, was associated with the reduction of LVM (Q = 15.20, df = 3, P = 0.0017). Highly trained/elite athletes had greater reductions in LVM compared with recreational and newly trained individuals (P < 0.01). Limitations included relatively few female participants and inconsistent reporting of intervention details. CONCLUSION: In summary, LVM is reduced following detraining of one week or more. Further research may provide a greater understanding of the effects of sex, age, and type of detraining on changes in LVM in endurance-trained individuals.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".