The Power Of Exercise: The Effect Of Age And Activity Level On Muscular Power
Bibliographic record
Abstract
Muscle, strength, and power decline as we age, and power is critical for functional independence. This dissertation tested additional factors such as the amount and type of PA, muscular fatigue, movement mechanics, and muscle fiber type, which are known to affect power, in older adults, including masters athletes. The study involved PA questionnaires, biomechanical assessment of functional tasks including countermovement jumps (CMJ) with lower-body motion capture and a custom apparatus with embedded force plates, and MRI Dixon and DTI of the lower limb musculature and lumbar spine region. Age, sex and PA level predicted lower-body power during CMJ, with activity level demonstrating a protective effect (r=0.540) similar in magnitude to the effect of age (r=-0.654). Athletics discipline also predicted lower-body power during CMJ (r=0.389) with short distance athletes having the highest predicted power but also the most negative slope. Greater trunk flexion was associated with greater lower-body power, but older adults did not tend to use this strategy. MRI diffusion parameters weakly predicted ankle power and also differed in short distance athletes. Activity level and athletic discipline showed positive, protective effects on lower-body and joint power during the CMJ with a strength of effect comparable to that of age. Trunk flexion angle was associated with greater lower-body power output in the CMJ and was a strategy adopted only by younger adults potentially confounding the measurement of power in older adults. Overall, high levels of PA, and participation in high power track and field events is protective of muscular power and likely functional independence in older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".