Heart Rate Changes Prior to Freezing of Gait Episodes Are Related to Anxiety
Bibliographic record
Abstract
BACKGROUND: Freezing of gait (FOG) in Parkinson's disease (PD) has been shown to be more frequent in stressful situations, implicating anxiety. Heart rate (HR) has been shown to increase prior to a FOG episode supporting the notion that elevated stress levels may trigger FOG. However, no studies to date have investigated whether elevated HR precedes all subtypes of FOG or only those episodes that are driven by anxiety. OBJECTIVE: The present study sought to investigate whether 1) HR increases prior to FOG episodes in nonspecific environments (Experiment 1), and if 2) HR increases prior to FOG when provoked in high but not low threat environments using a virtual reality paradigm (Experiment 2). METHODS: In Experiment 1, 10 of 19 participants with PD and FOG (PD + FOG) experienced FOG during a series of walking trials. In Experiment 2, 12 of 23 participants with PD + FOG experienced FOG while walking across an elevated and ground level narrow plank in virtual reality. HR was collected throughout the duration of both experiments, while FOG was quantified by experts using video review and tagging. RESULTS: HR significantly increased 2-3 seconds prior to a FOG episode during Experiment 1. In Experiment 2, HR significantly increased 4-6 seconds prior to a FOG episode, specifically while navigating the elevated plank. However, there were no significant increases in HR prior to FOG episodes when participants navigated the ground plank. CONCLUSION: This study extends previous work further demonstrating that increases in HR prior to FOG episodes appear linked to elevated anxiety levels.
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 | 0.001 |
| 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) | 0.001 | 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".