Does length of time since diagnosis in Parkinson’s disease influence heart rate variability? A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Intrinsic changes in Parkinson's disease (PD) affect the autonomic nervous system, and the disease course can aggravate the initial condition. Although the impact of time since disease onset on autonomic modulation has already been studied in other populations, this has not yet been investigated in PD. OBJECTIVE: To investigate the impact of the length of time since diagnosis on the cardiac autonomic modulation of individuals with PD and compare with healthy individuals. METHODS: Fifty participants were divided into three groups: a control group (CG; n = 24) and two groups with PD, divided according to the median length of time since diagnosis (median = 5.5 years): below the median (PG1; n = 13) and above the median (PG2; n = 13). To evaluate cardiac autonomic modulation, heart rate was obtained beat-to-beat in the supine position over a 30-min period, and heart rate variability (HRV) indices were calculated using linear methods in the time and frequency domains. RESULTS: There were no significant differences in HRV indices between the PG groups, or between the three groups regarding Mean RR, LFun, HFun and LF/HF ratio. Significant reductions in the RMSSD, SDNN, pNN50, LFms2 and HFms2 indices were observed in PG1 and PG2, compared with CG. CONCLUSIONS: The cardiac autonomic modulation of individuals with PD was not influenced by the time since diagnosis. However, reduced parasympathetic and global modulation were observed in these individuals, compared with controls. These results emphasize the importance of aerobic exercise for improving autonomic modulation 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".