Influence of Resistance Training Variables to Improve Muscle Mass Outcomes in Sarcopenia: A Systematic Review With Meta‐Regressions
Bibliographic record
Abstract
BACKGROUND: Resistance training (RT) is the first-line treatment to improve sarcopenia features. However, increasing muscle mass with RT remains challenging and displays inconsistent results. Manipulating training variables may present a novel approach to improve muscle mass gain in sarcopenic individuals. The present study aimed to measure the effectiveness of RT alone on muscle mass outcomes in older adults with sarcopenia and determine the influence of RT variables on muscle mass improvement. METHOD: We conducted a systematic review according to PRISMA standards to gather studies that conducted a supervised RT without nutritional intervention in sarcopenia-diagnosed older adults with a muscle mass outcome versus a control group. A search strategy was performed on PubMed, Medline, Cochrane and Google Scholar in the last 14 years, from the publication of the first agreement on the diagnosis (EWGSOP, 2010). Along with sample characteristics, we extracted and analysed the following training variables: frequency (number of sessions per week), intensity (in rating perceived effort and in % of the one repetition maximal load), duration (weeks), volume (number of sets per week), periodization (yes/no) and muscle failure (yes/no). First, we standardized the outcome with Hedge's g and pooled the effect size (ES) of each study in a univariate meta-analysis adjusted for risk of bias. Then, we performed training composition comparisons between 'effective interventions' and 'ineffective interventions', which were previously classified based on the 95% confidence interval (CI) effect size. Finally, relevant variables were regressed as moderators of the weighted ES in a mixed-effects model. RESULTS: A total of 14 studies representing 528 individuals (73.1 ± 6.6 years, 385 women [73%] and 143 men [27%]) were included for analysis. A significant effect of RT to improve muscle mass was found with a small weighted ES estimate (g = 0.38 [0.18; 0.58] 95% CI, p ≤ 0.001). There was no publication bias across studies (p = 0.7). 'Ineffective interventions' included significantly older individuals (p ≤ 0.01). Training composition was homogenous between the groups. The final model showed that age was the only significant moderator of the ES (estimate = -0.06 [-0.08; -0.03] 95% CI, p ≤ 0.001). CONCLUSION: In sarcopenic older adults, designing an evidence-based RT induces significant gains in muscle mass, but training variables manipulation does not yield greater outcomes. This study also unveils that ageing with sarcopenia negatively affects the significant improvement of muscle mass induced by RT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".