The Relationship Between Physical Activity, Cardiovascular Fitness, Muscle Strength and Age-related Change In Body Composition
Bibliographic record
Abstract
Maintaining muscle mass (MM) and bone mineral density (BMD) are the critical factors for independent living in elderly people. To maintain good health, a high level of daily activity or endurance training with a moderate intensity is recommended to improve cardiovascular fitness. However, it is unclear whether being physically active and to improve the cardiovascular fitness can suppress the age-related decrease of MM and BMD. Higher intensity training, such as resistance training, may be necessary. PURPOSE: To investigate the relationship between physical activity (PA), cardiovascular fitness (CF), muscle strength (MS) and age related change in MM and BMD. METHODS: 207 female subjects (19–78yrs) participated in this study. MM and BMD for the whole body were determined by using DXA. Maximal oxygen consumption (VO2max), handgrip strength and total weekly physical activity evaluated by the International Physical Activity Questionnaire (IPAQ) were determined as the indices of the CF, MS, and PA, respectively. Participants were divided into three age groups: 19–39yr, young-aged group; 40–59 yr, middle-aged group; over 60yr, old-aged group. They were also divided into two subgroups according to the level of CF, MS and PA at each age group: high (HCF) and low (LCF) CF group, high (HMS) and low (LMS) MS group, and high (HPA) and low (LPA) PA group. We used the Japanese standard score (CF and MS) and the intermediate value of all participants (PA) to divide the groups. A two-way ANO VA experimental design with a post-hoc test was adopted to analyze the data. RESULTS: Total MM and BMD decreased with aging (p0.05), while the factor of age was significant in both ANOVA tests (p0.05) than in LMS in the middle-aged (HMS vs. LMS; 37.5 ± 4.0 kg vs. 33.0 ± 3.5kg, p < 0.05) and old-aged group (HMS vs. LMS; 33.6 ± 2.5kg vs. 31.2 ± 2.8kg, p < 0.05). Quantitatively similar results were obtained for BMD. CONCLUSIONS: There was a significant difference between HMS and LMS in terms of MM for each age group, but there was no difference in the level of CF and/or PA in all age groups. This suggests that it is necessary to improve or maintain muscle strength by resistance training to keep muscle mass and BMD at high levels. Also MM and BMD could not be maintained at high level only by improving cardiovascular fitness and increasing physical activity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".