Resistance Training and Weight Loss in Older Adults: A Scoping Review
Bibliographic record
Abstract
Resistance training (RT) is one of the most effective interventions available to older adults wishing to slow the progressive loss of muscle size and strength known to occur with age. Less is known about the ability of RT to resist the onset of an equally problematic condition related to increased age: obesity. The objective of this scoping review was to characterize current research associated with RT and weight loss in older adults, including protocols, feasibility, and gaps in current knowledge. We searched six databases using variations of the terms "resistance training," "weight loss," and "older adults" for experimental or quasi-experimental studies published in the year 2009 or later. Studies were included if they featured at least one treatment group with a mean age of > 65 years that engaged in an RT-only exercise protocol with no aerobic or high-intensity interval component. Of the 6102 references identified by the initial database search, 24 were retained for analysis. Older women and older adults with obesity or sarcopenic obesity were the most studied groups (n = 13), followed by healthy community-dwelling older adults (n = 11) and studies involving older adults and some aspect of either dietary control or pharmaceutical intervention (n = 8). Significant between-study heterogeneity was observed in the RT characteristics researchers thought optimal for improving body composition measures in older adults. Changes in body composition, rather than total body mass, were found to be the essential variables to consider when evaluating the effectiveness of an RT intervention aimed at reducing chronic disease in older adults. Weight loss alone appears to be an incomplete and problematic outcome measure for older adults, with changes in body composition (ratio of fat mass to lean mass) being the more appropriate variable to emphasize in this population. However, it is important to note that only one study, showing questionably reproducible findings, found a significant lean body mass gain. The lack of abundant high-quality evidence demonstrating combined RT and a healthy diet can lead to significant fat loss and lean body mass gain, coupled with high attrition rates observed in many of the studies reviewed, highlight the need for further rigorous research.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".