Motor control and learning theories in the study of balance: A scoping review
Bibliographic record
Abstract
Balance control is an essential skill in stance and gait. Gaining a better understanding of balance will aid in developing new rehabilitation techniques and decrease the risk of falls. The field of motor behavior has many well-established theories that have influenced clinical practice and can be applied to study balance control. The purpose of our study is to conduct a scoping review of studies related to balance control that have used the following concepts: Fitts' law, focus of attention and challenge point framework (CPF). A comprehensive search of databases was performed to identify studies related to our purpose. Results show 47 studies that are related to our scope: 2 studies related to CPF, 12 studies related to Fitts' law; and 33 studies related to focus of attention. The majority of studies involved young adult participants (n=38), with a minority of studies (~19%) involving special populations who have standing balance impairments. Our review provides evidence that motor behavior theories can be applied to better understand balance control. Fitts' law was used repeatedly to design alterable levels of task difficulty and was found to have a relationship with anticipatory postural adjustments. The CPF was utilized effectively to design balance rehabilitation programs. Evidence related to focus of attention showed that adopting an external focus of attention yields better performance and learning of balance-related tasks. The majority of studies reviewed involved non-disabled healthy populations. Future research should focus on implementing motor behavior concepts in clinical settings to examine balance control among people with balance impairments.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".