Age-Related Modifications of Muscle Synergies during Daily-Living Tasks: A Scoping Review
Bibliographic record
Abstract
Abstract Purpose Aging is associated with changes in neuromuscular control that can lead to difficulties in performing daily-living tasks. Based on electromyography, muscle synergy analysis provides a valuable tool for assessing neuromuscular control strategies. However, the age-related changes of muscle synergies during daily living tasks are scattered throughout the literature. This review aimed to synthesize the existing literature on muscle synergies in elderly people during daily-living tasks and examine how they differ from those exhibited by young adults. Methods A search was conducted across the Medline, CINHAL, and Web of Science databases. Studies were included if they focused on muscle synergies in elderly people during walking, sit-to-stand or stair ascent, and if muscle synergies were obtained by a matrix factorization algorithm. Results The research strategy identified 4849 studies, of which 17 studies were included after the screening process. The muscle synergies of 295 elderly people and 182 young adults were reported. Results suggest that: 1) elderly people and young adults retain similar muscle synergies’ number, 2) elderly people have higher muscles contribution during walking, and 3) an increased inter and intra-subject variability during specific tasks (i.e., walking and stair ascent, respectively) was reported in elderly people compared to young adults. Conclusion This review provides a comprehensive understanding of age-related changes in neuromuscular control during daily-living tasks. Our findings suggested that although the number of synergies remains similar, metrics such as spatial and temporal structures of synergies are more suitable to identify neuromuscular control deficits between young adults and elderly people.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".