The Experience of OFF Periods in Parkinson’s Disease: Descriptions, Triggers, and Alleviating Factors
Bibliographic record
Abstract
PURPOSE: Wearing off of Parkinson's disease medication is common, but triggers and coping strategies for this transient phenomenon are poorly understood. We aimed to assess the lived experience of OFF periods for people with Parkinson's disease. METHODS: Participants in the longitudinal Fox Insight study who endorsed OFF periods were invited to complete a survey consisting of both multiple-choice and free-text responses. Descriptive statistics were used to summarize multiple-choice responses, and free-text responses were classified into themes through iterative discussion by 3 movement disorders specialists. RESULTS: A total of 2110 participants (52.4% male) completed the survey. Tremor was the most common description of OFF periods (n=1038, 49.2%), followed by gait changes (n=535, 25.4%) and rigidity (n=430, 20.4%). Of 1498 specific triggers for OFF symptoms, the most common was stress (n=920, 61.4%), followed by anxiety/depression (n=476, 31.8%) and tiredness/fatigue (n=351, 23.4%). Common coping strategies (n=1416 responses) included exercise (n=678, 47.9%), taking a break (n=504, 35.6%), and meditation (n=276, 19.5%). CONCLUSIONS: Although OFF periods are common, the individual experiences of OFF vary. This knowledge could be used to develop new counseling strategies for OFF periods in people with Parkinson's disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".