The effects of plantar surface stimulation on neuromuscular responses during standing balance and gait in healthy and clinical populations: A systematic review
Bibliographic record
Abstract
Enhanced sensory input applied to the plantar surface of the feet, through innovative sensory-stimulating footwear devices, has the potential to improve standing balance and gait. However, the underlying mechanisms by which sensory-stimulating footwear devices bring about their effects on balance and gait remain unclear: changes in neuromuscular control are one possible option. This systematic review aimed to synthesize the effects of enhanced plantar surface stimulation, via footwear, shoe inserts, or floor surfaces, on neuromuscular responses during standing balance and walking gait in healthy and clinical populations. Four databases were selected to identify studies of plantar stimulation devices designed to alter neuromuscular responses during balance or gait. 2773 articles were screened for eligibility based on title and abstract. Thereafter, 37 articles underwent full text review, of which 17 articles were included in the review. The evidence that enhanced plantar sensory stimulation alters muscle activity during standing balance is limited, and during gait is moderate. The effects of sensory-stimulating footwear devices also appear to be dependent on factors such as the target population, the location of the sensory stimuli under the foot, or the complexity of the postural or walking task.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".