Effects of High-Intensity Interval Training on Muscle Strength for the Prevention and Treatment of Sarcopenia in Older Adults: A Systematic Review of the Literature
Bibliographic record
Abstract
Sarcopenia is a significant health concern primarily affecting old adult individuals, characterized by age-related muscle loss, and decreased strength, power, and endurance. It has profound negative effects on overall health and quality of life, including reduced independence, mobility, and daily activity performance, osteoporosis, increased fall and fracture risks, metabolic issues, and chronic diseases like diabetes and cardiovascular conditions. Preventive strategies typically involve a combination of proper nutrition and regular physical activity. Among strength training exercises, high-intensity interval training (HIIT) stands out as the most effective approach for improving muscle function in older adults with sarcopenia. The current review identifies and summarizes the studies that have examined the effects of HIIT on muscle strength in older adults as an element of the prevention and treatment of sarcopenia. A systematic search using several computerized databases, namely, MEDLINE/PubMed, Scopus, SPORTDiscus, and Web of Science, was performed on 12 January 2023, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 224 studies were initially retrieved. A total of five studies met the selection criteria. HIIT training shows improvements in body composition and functional and cardiorespiratory capacity, has benefits on muscle strength, increases muscle quality and architecture, and is associated with muscle hypertrophy in healthy older adults. Nonetheless, given the shortcomings affecting primary research in terms of the limited number of studies and the high risk of bias, further research is warranted.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".