Triggers and alleviating factors for fatigue in Parkinson’s disease
Bibliographic record
Abstract
BACKGROUND: Fatigue is common in Parkinson's Disease, but few effective treatments are available for it. Exploring triggers and alleviating factors, including effects of exercise, could inform development of management strategies for Parkinson's Disease fatigue. OBJECTIVES: To examine triggers and alleviating factors for fatigue reported by individuals with Parkinson's Disease, including perceived effect of exercise. METHODS: A sample of individuals with self-reported Parkinson's Disease participating in the study Fox Insight were administered an online survey. The survey included the Parkinson's Fatigue Scale, the Physical Activity Scale for the Elderly, and multiple-choice questions about triggers and alleviating factors for fatigue. RESULTS: Among the sample of 1,029 individuals with Parkinson's disease, mean (standard deviation (SD)) age was 67.4 (9.3) years, 44.0% were female. Parkinson's Fatigue Scale score ranged from 16-80, mean (SD) 48.8 (16.2). Poor sleep (62.1%) and physical exertion (45.1%) were frequently reported triggers for fatigue. Coping strategies including sitting quietly (58.1%), laying down with or without napping, and exercise (20%). Physical Activity Scale for the Elderly scores were higher in those who reported that exercise alleviated their fatigue (49.7%) compared to those who reported it worsened their fatigue (18.9%) (mean (SD) score 158.5 (88.8) vs 119.8 (66.6) respectively; p<0.001). CONCLUSIONS: Several behavioral and environmental triggers and alleviating strategies for fatigue are reported by individuals with Parkinson's disease. Many feel that exercise alleviates fatigue, though the relationship between exercise and fatigue in Parkinson's Disease appears complex. This exploratory study may inform future development of treatments or coping strategies for Parkinson's disease fatigue.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".