Growing older with health and vitality: a nexus of physical activity, exercise and nutrition
Bibliographic record
Abstract
The preservation of skeletal muscle mass and strength with advancing age are, we propose, critical aspects of ageing with health and vitality. Physical inactivity and poor nutrition are known to accelerate the gradual age-related decline in muscle mass and strength-sarcopenia-however, both are subject to modification. The main purpose of this review is to present the latest, evidence-based recommendations for physical activity and exercise, as well as diet for older adults that would help in preserving muscle mass and strength. We take the position that future physical activity/exercise guidelines need to make specific reference to resistance exercise and highlight the benefits of higher-intensity aerobic exercise training, alongside advocating older adults perform aerobic-based physical activity and household tasks (e.g., carrying groceries). In terms of dietary recommendations, greater emphasis should be placed on optimal rather than minimum protein intakes for older adults. Indeed, guidelines that endorse a daily protein intake of 1.2-1.5 g/kg BM/day, which are levels 50-90 % greater than the current protein Recommendation Dietary Allowance (0.8 g/kg BM/day), are likely to help preserve muscle mass and strength and are safe for healthy older adults. Being cognisant of factors (e.g., reduced appetite) that may preclude older adults from increasing their total daily protein intake, we echo the viewpoint of other active researchers in advocating that protein recommendations for older adults be based on a per meal approach in order to maximize muscle protein synthesis (MPS). On this basis, assuming three meals are consumed daily, a protein dose of 0.4-0.5 g/kg BM should be contained in each meal. We are beginning to understand ways in which to increase the utilization of ingested protein for the stimulation of MPS, namely by increasing the proportion of leucine contained in a given dose of protein, co-ingesting other nutrients (e.g., carbohydrate and fat or supplementation with n-3 polyunsaturated fatty acids) or being physically active prior to protein intake. Clearly, developing simple lifestyle interventions targeted at preserving muscle mass and strength with advancing age is crucial for facilitating longer, healthier lives into older age.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".