Measuring Hand Sensory Function and Force Control in Older Adults: Are Current Hand Assessment Tools Enough?
Bibliographic record
Abstract
BACKGROUND: The ability to grasp and manipulate objects is essential for performing activities of daily living. However, there is limited information regarding age-related behavioral differences in hand sensorimotor function due, in part, to the lack of assessment tools capable of measuring subtle but important differences in hand function. The purpose of this study was to demonstrate performance differences in submaximal force control and tactile pattern recognition in healthy older adults using 2 custom-designed sensorimotor assessment tools. METHODS: Sensorimotor function was assessed in 13 healthy older adults (mean age 72.2 ± 5.5 years, range: 65-84 years) and 13 young adults (mean age 20 ± 1.4 years, range: 19-23 years). Clinical assessments included the Montreal Cognitive Assessment (MoCA), monofilament testing, maximum voluntary contraction (MVC), and Grooved Pegboard Test. Sensorimotor assessments included submaximal (5, 20% MVC) grip force step-tracking and tactile pattern recognition tasks. RESULTS: Clinical assessments revealed no or minimal group differences in MVC, monofilament thresholds, and MoCA. However, sensorimotor assessments showed that older adults took longer to discriminate tactile patterns and had poorer accuracy than young adults. Older adults also produced submaximal forces less smoothly than young adults at the 20% force level while greater variability in force maintenance was seen at 5% but not 20% MVC. CONCLUSIONS: These results demonstrate the ability to integrate higher-order tactile information and control low grip forces is impaired in older adults despite no differences in grip strength or cognition. These findings underscore the need for more sensitive evaluation methods that focus on sensorimotor ability reflective of daily activities.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".