A novel cognitive cueing approach to gait retraining in Parkinson’s disease: A pilot study
Bibliographic record
Abstract
Background: Parkinson’s disease (PD) impairs gait performance, which can lead to falls and decreased quality of life. This study examined the feasibility of implementing a novel home-based intervention designed to elicit gait improvement in individuals with PD. Methods: Five participants with PD completed a two-week home-based gait retraining intervention designed around guided video feedback. Semi-structured interviews were conducted postintervention and two months postintervention to acquire feedback from the participants about their experience with the intervention. Spatiotemporal parameters of gait and functional mobility were assessed pre and postintervention and at two months postintervention. Results: Participants reported high levels of usability and expressed they believed that the intervention improved their gait and led to a fortified sense of ability and revived sense of empowerment. Comparisons of spatiotemporal and mobility parameters of gait identified that improvements occurred between preintervention and postintervention—step length (x̄ = 10.7%), gait velocity (x̄ = 15.1%), and TUG scores (x̄ = 9.8%)—and between preintervention and two months postintervention—step length (x̄ = 3.9%), gait velocity (x̄ = 9.9%), and TUG scores (x̄ = 4.2%). Conclusions: Guided home-based video training has potential to be an effective treatment strategy for improving gait impairment among individuals with PD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".