Postural Adjustments and Kinematic Index Finger Features in Frail Older Adults under Different Equilibrium Constraints
Bibliographic record
Abstract
Background: Anticipatory postural adjustments (APAs) are significantly affected by age and may represent restrictions on functional independence. Previous studies in young adults have already highlighted that changing postural stability (i.e., seated vs. upright posture) affects the motor planning and APAs. In frail older adults (FOAs), the effect of these different conditions of postural stability have not yet been established, and the present study aimed to disentangle this issue. Methods: Participants executed an arm-pointing task to reach a diode immediately after it turned on, under different conditions of stability (seated with and without foot support and in an upright posture). A kinematic profile of the index finger and postural electromyographic data were registered in their dominant-side leg muscles: tibialis anterior, soleus, rectus femoris, and semitendinosus. Results: The main finding of this study was that the adopted posture and body stabilization in FOAs did not reflect differences in APAs or kinematic features. In addition, they did not present an optimal APA, since postural muscles are recruited simultaneously with the deltoid. Conclusion: Thus, FOAs seem to use a single non-optimal motor plan to assist with task performance and counterbalance perturbation forces in which they present similar APAs and do not modify their kinematics features under different equilibrium constraints.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".