Using Measures Of Fatigue To Discriminate Between Levels Of Self-reported Functional Ability.
Bibliographic record
Abstract
The role of lower limb strength in functional ability is well known. Whether fatigue can be used to dissociate between levels of functional ability remains to be established. PURPOSE: Determine if measures of muscle fatigue and subjective fatigue can discriminate between different levels of functional ability in older women. METHODS: Twenty-eight Caucasian older women were divided into two groups (high and moderate/low functioning) based on self-reported functional ability. Functional ability was assessed with the Composite Physical Function scale. Fourteen women were high functioning (72 ± 5 yrs) and 14 were of moderate to low functioning (75 ± 3 yrs). Isometric hand grip strength and fatigue, as well as isometric and isotonic leg extension strength and fatigue were compared between groups. Field-based tests of walking speed, 8ft up-and-go, 30-seconds arm curl, 30-seconds chair stands, and subjective fatigue (mobility-tiredness scale and two items from the WHO Quality of Life Questionnaire) were also compared. Hand grip and leg extension isometric fatigue was defined as the time when force decreased to 50% of maximum. Isotonic leg extension fatigue was a percentage decline in maximum isometric strength following 3 sets of 10 isotonic leg extensions with ankle weights at 70% of 1RM maximum. RESULTS: Leg extension strength (p<0.05), walking speed (p<0.01), and performance for 8ft up-and-go and 30-second chair stands test (p<0.001), were greater in high functioning women compared to the moderate/low functioning women. Subjective fatigue was greater (p<0.05) in the moderate/low functioning women compared to the high functioning group. No significant differences were found between the two groups for hand grip strength (p=0.23), hand grip fatigue (p=0.18), leg extension fatigue (p=0.31), and performance for the 30-seconds arm curl test (p=0.15). CONCLUSIONS: Lower limb tests of muscle strength and mobility were better able to discriminate between functional ability levels compared with upper limb tests. Objective measurements of muscle fatigue, in both upper and lower limbs, did not influence self-reported functional ability scores in these older women, however, measurements of subjective fatigue were related to functional ability. Supported by NSERC
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".