Ankle Bracelet Laser as a Novel Portable Device to Improve Walking in Patients With Parkinsonism: Randomized Crossover Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Freezing of gait (FOG) is a common and debilitating symptom of parkinsonism. Although visual cues have proven efficacy in alleviating FOG, most current visual cues are fixed in place, restricting their use to controlled environments such as clinics or homes. Mobile open-loop cueing devices have been developed to address this limitation; however, they typically require manual activation to deliver the visual cues, which can be particularly challenging for patients with attention or cognitive impairments, leading to equivocal results in improving gait performance. OBJECTIVE: The aim of the study is to assess the efficacy of an ankle bracelet laser, a new mobile visual cue designed for practical use, in improving gait performance in patients with parkinsonism and FOG. METHODS: A randomized controlled 2-period crossover trial was conducted from June 15, 2020, to October 1, 2020, at Ramathibodi Hospital. In total, 10 patients with parkinsonism and FOG were enrolled in 2 conditions: walking with laser-off first and walking with laser-on first. Gait speed, the timed up and go test, stride length, and the locomotor rehabilitation index were assessed twice in each trial with a 10-minute washout period. RESULTS: The results showed favorable improvement in all parameters. Gait speed and stride length improved by 0.07 m/s (95% CI 0.04-0.09 m/s; P<.001) and 0.17 m (95% CI 0.11-0.23 m; P<.001), respectively, with laser-on. The timed up and go test duration was reduced by 7.69 seconds (95% CI 2.82-12.55 seconds; P=.002). The locomotor rehabilitation index improved by 4.46% (95% CI 2.56%-6.36%; P<.001). When using the device, there were no adverse effects, such as dizziness or blurred vision. CONCLUSIONS: The ankle bracelet laser cue produced immediate improvements in gait speed, stride length, and balance in patients with parkinsonism and FOG, suggesting that the device can acutely enhance gait performance. Further research is needed to determine whether these benefits are sustained and applicable to daily life activities. TRIAL REGISTRATION: Thai Clinical Trials Registry TCTR20210511001; https://www.thaiclinicaltrials.org/show/TCTR20210511001.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".