The Impact of Electrical Stimulation and Exercise on Independent Static Standing Balance
Bibliographic record
Abstract
Purpose: Maintaining balance requires a complex integration of input from multiple sensory systems. Studies have shown positive effects of using transcutaneous electrical stimulation (TENS) and neuromuscular electrical stimulation (NMES) to enhance somatosensory feedback and muscular strength associated with balance. The purpose of this study is to examine the effects of electrical stimulation on independent standing balance during single leg stance (SLS) using either NMES with exercise, TENS with exercise, or exercise alone.\nSubjects: Fourteen subjects were recruited through a convenience sample on the University of Puget Sound campus.\nMethods: Randomized control trial. Subjects participated in this study five times per week for a total of six weeks. Participants were randomly assigned into each group: NMES with home exercise program (HEP),TENS with HEP and HEP-only. The experimental groups performed 60 minutes of electrical stimulation. All groups received the same HEP. SLS balance assessment was performed on each participant at one and six weeks.\nResults: Change in SLS over time showed no significant difference (p=0.67; power=0.10). There was no significant difference between groups (p=0.96; power=0.05). There was a significant difference in SLS time between eyes open versus eyes closed (p\nConclusions: There was no significant difference in SLS time with the use of NMES, TENS or exercise alone.\nRelevance: This study suggests that applying electrical stimulation with described protocols may not have an effect on independent static standing balance. Further research should be done that incorporates other protocols and parameters.
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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 | 0.001 |
| 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) | 0.003 | 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".