Managing fatigue in Parkinson's disease: Preparing for a randomized controlled trial
Bibliographic record
Abstract
Background Fatigue in Parkinson's disease (PD) is a common, debilitating symptom often overlooked in research and clinical practice. Effective interventions are needed to mitigate its impact on people with PD. Objective This pilot study evaluated the feasibility of the individual videoconference version of the Packer Managing Fatigue program for people with PD and explored its preliminary effectiveness versus usual care to inform the design of a definitive trial. Here we report on the second objective. Methods A two-arm, assessor-masked, randomized controlled pilot study recruited participants with PD who experience severe fatigue, have English proficiency, and internet access. Outcome measures included occupational performance, satisfaction with performance, occupational balance, fatigue impact, quality of life, and sleep. Mixed repeated-measures ANOVA and non-parametric tests were used for analysis. Results Mixed-design ANOVA (N = 25) showed an exploratory trend toward significant for the Time × Group interaction effect differences in satisfaction with performance between groups over time ( p = 0.09). Paired t-tests within the intervention group indicated significant improvement in satisfaction with performance ( p = 0.04). The effect size for this outcome was moderate. Small to moderate effect sizes were observed for occupational balance, occupational performance, and subscales of the Multidimensional Fatigue Inventory. Other measures showed negligible effects. Conclusions The results provide preliminary evidence of the program's benefits for people with PD. Larger, more rigorous studies are needed to confirm its effectiveness. Despite the small sample size and challenges posed by COVID-19, this study offers valuable insights into recruitment strategies and effect sizes to inform future trial designs.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".