Ankle dorsiflexors and plantarflexors neuromuscular electrical stimulation training impacts gait kinematics in older adults: A pilot study
Bibliographic record
Abstract
BACKGROUND: While ankle muscles, highly affected by aging, are highly implicated in the changes in gait kinematics and involved in the limitation of seniors' mobility, whether neuromuscular electrical stimulation (NMES) training of these muscles could impact gait kinematics in older adults has not been investigated yet. RESEARCH QUESTION: What are the effects of 12 weeks of ankle plantar and dorsiflexors NMES training on strength and gait kinematics in healthy older adults? METHODS: Fourteen older adults (73.6 ± 4.9 years) performed a three-time per week, three months long NMES training of both ankle plantar and dorsiflexors. Before and after training, neuromuscular parameters, gait kinematic parameters, and daily physical activity were measured. RESULTS: The participants significantly increased their lower limb muscle mass and their plantar and dorsiflexors isometric strength after training. They reduced the hip abduction/adduction and the pelvic anterior tilt range of motion and variability during gait. However, the participants became less active after the training. SIGNIFICANCE: NMES training of ankle muscles, by increasing ankle muscle mass and strength,modified gait kinematics. NMES training of ankle muscles is feasible and effective to lower the hip implication and increment foot progression angle during gait. Further study should determine if this could lower the risk of falling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".