Enhancing Cardiovascular Autonomic Regulation in Parkinson’s Disease Through Non-Invasive Interventions
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) often involves autonomic dysfunction, most notably impaired baroreflex sensitivity (BRS), which disrupts cardiovascular homeostasis and contributes to orthostatic hypotension (OH). Pharmacological and invasive treatments, including deep brain stimulation, have yielded inconsistent benefits and carry procedural risks, highlighting the need for safer, more accessible alternatives. In this systematic review, we evaluated non-invasive interventions-spanning somatosensory stimulation, exercise modalities, thermal therapies, and positional strategies-aimed at improving cardiovascular autonomic function in PD. METHODS: = 8) including 205 participants met the inclusion criteria for analyzing cardiac sympathovagal balance. RESULTS: Five studies demonstrated significant post-intervention increases in BRS. Most reported favorable shifts in heart rate variability (HRV) and favorable changes in the low-frequency/high-frequency (LF/HF) ratio. Across modalities, systolic blood pressure (SBP) decreased by an average of 5%, and some interventions produced benefits that persisted up to 24 h. CONCLUSION: Although sample sizes were small and protocols heterogeneous, the collective findings support the potential of non-invasive neuromodulation to enhance BRS and overall cardiovascular regulation in PD. Future research should focus on standardized, higher-intensity or combined protocols with longer follow-up periods to establish durable, clinically meaningful improvements in autonomic function and quality of life for people living 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".