Effects of old age on fatigability and sensorimotor characteristics of a repetitive upper limb fatiguing task
Bibliographic record
Abstract
OBJECTIVES: 1) Determine the effects of old age on sensorimotor responses to a fatiguing work-like task. 2) Explore how old age influences the relationships between task fatigability, everyday perceptions of fatigability, and sensorimotor function. METHODS: Healthy young (N = 17, 9W) and older (N = 13, 10W) adults completed the Pittsburgh Fatigability Scale to assess everyday perceptions of physical (PF) and mental fatigability and performed a repetitive tapping task to fatigue. Before and after the task, grip strength was assessed using a hand-grip dynamometer and touch-pressure sensitivity was measured (shoulder, hand) using Semmes-Weinstein monofilaments. RESULTS: Older, but not young adults, had increased touch-pressure sensitivity at the shoulder after fatigue (interaction, p = 0.007). No changes in grip strength were observed (p>0.05). Task fatigability was not different between young and old adults (p>0.05). Having less task fatigability was associated with lower PF, higher grip strength, and higher touch-pressure sensitivity at the hand (ρ = 0.37-0.58, p<0.05), with the hand sensation association also observed in the old adult subgroup (ρ = 0.56, p = 0.046). CONCLUSIONS: With old age, there were fatigue-related alterations to sensory but not physical function. While task fatigability was associated with perceptual, physical, and sensory features, sensory features appear to have a more important role with old age.
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 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 | 0.002 |
| 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) | 0.001 | 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".