Textured Foot Orthotics on Dynamic Stability and Turning Performance in Parkinson’s Disease
Bibliographic record
Abstract
The purpose of this study was to facilitate sensory feedback, with textured foot orthotics, to evaluate dynamic stability and turning behavior in Parkinson’s disease individuals. Seven participants with a diagnosis of idiopathic Parkinson’s disease, aged 55–80 years old, participated in this study. Participants completed three testing sessions; baseline, 4 weeks post-baseline, and 5 weeks post-baseline. Three experimental conditions were tested: footwear only (F), footwear + non-textured orthotic (FO), and footwear + textured orthotic (FOT). Kinematic, kinetic, and video data were collected during the steps preceding a turn task. Variables of interest included dynamic stability (maximum mediolateral (ML), minimum ML, and ML range of the center of mass (COM)-base of support (BOS) relationship) and turning performance (gait velocity and step count). There was a statistically significant increase in maximum ML COM-BOS distance (week 4 [0.1298 m ± 0.054] compared to week 0 [0.1069 m ± 0.050] p = .0076), and a significant decrease in step count (week 0-F [5.52 steps ± 1.08] to week 0-FO [5.23 steps ± 0.87] p = .0296) and (week 4-FO [5.24 steps ± 1.31] to week 4–FOT [4.67 steps ± 0.76] p = .0004). Textured foot orthotics modified dynamic stability and turning performance in Parkinson’s disease individuals completing a 180° degree turn. These preliminary results support this potential treatment option for rehabilitation professionals treating Parkinson’s disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".